Nothing
tests/testthat/_snaps/coxph.md
In JointAI: Joint Analysis and Imputation of Incomplete Data
jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- 0
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
}
$m1a
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] +
M_lvlone[i, 5] * beta[2]
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
}
$m1b
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] +
M_lvlone[i, 5] * beta[2]
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
}
$m2a
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1]
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
# Normal model for copper -------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- M_lvlone[i, 4] * alpha[1]
}
# Priors for the model for copper
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
}
$m3a
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] +
M_lvlone[i, 6] * beta[2] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[3] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[4] +
(M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * beta[5]
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
# Normal model for trig ---------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, )
mu_trig[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[4]
M_lvlone[i, 9] <- log(M_lvlone[i, 3])
}
# Priors for the model for trig
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_trig <- sqrt(1/tau_trig)
# Normal model for copper -------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- M_lvlone[i, 5] * alpha[5] + M_lvlone[i, 6] * alpha[6] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[7]
M_lvlone[i, 8] <- abs(M_lvlone[i, 7] - M_lvlone[i, 4])
}
# Priors for the model for copper
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
}
$m3b
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- b_Srv_ftm_stts_cn_center[group_center[i], 1] +
beta[1] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[2] * M_lvlone[i, 5] +
beta[3] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[4] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] +
beta[5] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
for (ii in 1:10) {
b_Srv_ftm_stts_cn_center[ii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[ii, ], invD_Srv_ftm_stts_cn_center[ , ])
mu_b_Srv_ftm_stts_cn_center[ii, 1] <- 0
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1])
# Normal mixed effects model for trig -------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, )
mu_trig[i] <- b_trig_center[group_center[i], 1] +
alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[3] * M_lvlone[i, 5] +
alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 8] <- log(M_lvlone[i, 3])
}
for (ii in 1:10) {
b_trig_center[ii, 1:1] ~ dnorm(mu_b_trig_center[ii, ], invD_trig_center[ , ])
mu_b_trig_center[ii, 1] <- M_center[ii, 1] * alpha[1]
}
# Priors for the model for trig
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_trig <- sqrt(1/tau_trig)
invD_trig_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_trig_center[1, 1] <- 1 / (invD_trig_center[1, 1])
# Normal mixed effects model for copper -----------------------------------------
for (i in 1:312) {
M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- b_copper_center[group_center[i], 1] + alpha[6] * M_lvlone[i, 5] +
alpha[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 7] <- abs(M_lvlone[i, 6] - M_lvlone[i, 4])
}
for (ii in 1:10) {
b_copper_center[ii, 1:1] ~ dnorm(mu_b_copper_center[ii, ], invD_copper_center[ , ])
mu_b_copper_center[ii, 1] <- M_center[ii, 1] * alpha[5]
}
# Priors for the model for copper
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
invD_copper_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_copper_center[1, 1] <- 1 / (invD_copper_center[1, 1])
}
$m4a
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (ii in 1:312) {
logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ])
eta_Srv_ftm_stts_cn[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[1] +
M_id[ii, 5] * beta[2] + M_id[ii, 6] * beta[3]
logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[4] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] +
M_lvlone[srow_Srv_ftm_stts_cn[ii], 3] * beta[6] +
M_lvlone[srow_Srv_ftm_stts_cn[ii], 4] * beta[7] +
M_lvlone[srow_Srv_ftm_stts_cn[ii], 5] * beta[8]
logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] +
(M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[4] +
(M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] +
M_lvlonegk[ii, 3, 1:15] * beta[6] +
M_lvlonegk[ii, 4, 1:15] * beta[7] +
M_lvlonegk[ii, 5, 1:15] * beta[8])
log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ])
phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii])
zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:8) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
}
$m4b
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (ii in 1:312) {
logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ])
eta_Srv_ftm_stts_cn[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[1] +
M_id[ii, 5] * beta[2] + M_id[ii, 6] * beta[3] +
M_id[ii, 7] * beta[4]
logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6]
logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] +
(M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] +
(M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6])
log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ])
phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii])
zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
}
$m4c
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (ii in 1:312) {
logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ])
eta_Srv_ftm_stts_cn[ii] <- b_Srv_ftm_stts_cn_center[group_center[pos_id[ii]], 1] +
beta[1] * (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] +
beta[2] * M_id[ii, 4]
logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4]
logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] +
(M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] +
(M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4])
log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ])
phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii])
zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii])
}
for (iii in 1:10) {
b_Srv_ftm_stts_cn_center[iii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[iii, ], invD_Srv_ftm_stts_cn_center[ , ])
mu_b_Srv_ftm_stts_cn_center[iii, 1] <- 0
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1])
}
$m4d
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (ii in 1:312) {
logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ])
eta_Srv_ftm_stts_cn[ii] <- b_Srv_ftm_stts_cn_center[group_center[pos_id[ii]], 1] +
beta[1] * (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] +
beta[2] * M_id[ii, 4]
logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5]
logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] +
(M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] +
(M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] +
(M_lvlonegk[ii, 3, 1:15] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5])
log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ])
phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii])
zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii])
}
for (iii in 1:10) {
b_Srv_ftm_stts_cn_center[iii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[iii, ], invD_Srv_ftm_stts_cn_center[ , ])
mu_b_Srv_ftm_stts_cn_center[iii, 1] <- 0
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1])
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a
Potential scale reduction factors:
Point est. Upper C.I.
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
copper NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
copper NaN NaN
sexfemale NaN NaN
age NaN NaN
abs(age - copper) NaN NaN
log(trig) NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
copper NaN NaN
sexfemale NaN NaN
age NaN NaN
abs(age - copper) NaN NaN
log(trig) NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
D_Srv_ftm_stts_cn_center[1,1] NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
trtplacebo NaN NaN
albumin NaN NaN
platelet NaN NaN
stage.L NaN NaN
stage.Q NaN NaN
stage.C NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m4b
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
trtplacebo NaN NaN
sexfemale:trtplacebo NaN NaN
albumin NaN NaN
log(platelet) NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m4c
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
albumin NaN NaN
log(platelet) NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
D_Srv_ftm_stts_cn_center[1,1] NaN NaN
$m4d
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
albumin NaN NaN
ns(platelet, df = 2)1 NaN NaN
ns(platelet, df = 2)2 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
D_Srv_ftm_stts_cn_center[1,1] NaN NaN
Code
lapply(models0, MC_error)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
est MCSE SD MCSE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
copper 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
copper 0 0 0 NaN
sexfemale 0 0 0 NaN
age 0 0 0 NaN
abs(age - copper) 0 0 0 NaN
log(trig) 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
copper 0 0 0 NaN
sexfemale 0 0 0 NaN
age 0 0 0 NaN
abs(age - copper) 0 0 0 NaN
log(trig) 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN
$m4a
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
trtplacebo 0 0 0 NaN
albumin 0 0 0 NaN
platelet 0 0 0 NaN
stage.L 0 0 0 NaN
stage.Q 0 0 0 NaN
stage.C 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m4b
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
trtplacebo 0 0 0 NaN
sexfemale:trtplacebo 0 0 0 NaN
albumin 0 0 0 NaN
log(platelet) 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m4c
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
albumin 0 0 0 NaN
log(platelet) 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN
$m4d
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
albumin 0 0 0 NaN
ns(platelet, df = 2)1 0 0 0 NaN
ns(platelet, df = 2)2 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2,
n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale
0 0
Call:
coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, I(status != "censored"))"
Coefficients:
age sexfemale
0 0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper
0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper sexfemale age abs(age - copper)
0 0 0 0
log(trig)
0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper sexfemale age abs(age - copper)
0 0 0 0
log(trig)
0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + trt + albumin + platelet + stage + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale trtplacebo albumin platelet stage.L stage.Q
0 0 0 0 0 0 0
stage.C
0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex * trt + albumin + log(platelet) + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale trtplacebo
0 0 0
sexfemale:trtplacebo albumin log(platelet)
0 0 0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + log(platelet) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale albumin log(platelet)
0 0 0 0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale albumin
0 0 0
ns(platelet, df = 2)1 ns(platelet, df = 2)2
0 0
$m0a
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2,
n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
$m1a
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale
0 0
$m1b
Call:
coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, I(status != "censored"))"
Coefficients:
age sexfemale
0 0
$m2a
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper
0
$m3a
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper sexfemale age abs(age - copper)
0 0 0 0
log(trig)
0
$m3b
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper sexfemale age abs(age - copper)
0 0 0 0
log(trig)
0
$m4a
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + trt + albumin + platelet + stage + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale trtplacebo albumin platelet stage.L stage.Q
0 0 0 0 0 0 0
stage.C
0
$m4b
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex * trt + albumin + log(platelet) + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale trtplacebo
0 0 0
sexfemale:trtplacebo albumin log(platelet)
0 0 0
$m4c
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + log(platelet) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale albumin log(platelet)
0 0 0 0
$m4d
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale albumin
0 0 0
ns(platelet, df = 2)1 ns(platelet, df = 2)2
0 0
Code
lapply(models0, coef)
Output
$m0a
$m0a$`Surv(futime, status != "censored")`
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m1a
$m1a$`Surv(futime, status != "censored")`
age sexfemale
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
age sexfemale
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m2a
$m2a$`Surv(futime, status != "censored")`
copper beta_Bh0_Srv_ftm_stts_cn[1]
0 0
beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3]
0 0
beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5]
0 0
beta_Bh0_Srv_ftm_stts_cn[6]
0
$m3a
$m3a$`Surv(futime, status != "censored")`
copper sexfemale
0 0
age abs(age - copper)
0 0
log(trig) beta_Bh0_Srv_ftm_stts_cn[1]
0 0
beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3]
0 0
beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5]
0 0
beta_Bh0_Srv_ftm_stts_cn[6]
0
$m3b
$m3b$`Surv(futime, status != "censored")`
copper sexfemale
0 0
age abs(age - copper)
0 0
log(trig) D_Srv_ftm_stts_cn_center[1,1]
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m4a
$m4a$`Surv(futime, status != "censored")`
age sexfemale
0 0
trtplacebo albumin
0 0
platelet stage.L
0 0
stage.Q stage.C
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m4b
$m4b$`Surv(futime, status != "censored")`
age sexfemale
0 0
trtplacebo sexfemale:trtplacebo
0 0
albumin log(platelet)
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m4c
$m4c$`Surv(futime, status != "censored")`
age sexfemale
0 0
albumin log(platelet)
0 0
D_Srv_ftm_stts_cn_center[1,1] beta_Bh0_Srv_ftm_stts_cn[1]
0 0
beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3]
0 0
beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5]
0 0
beta_Bh0_Srv_ftm_stts_cn[6]
0
$m4d
$m4d$`Surv(futime, status != "censored")`
age sexfemale
0 0
albumin ns(platelet, df = 2)1
0 0
ns(platelet, df = 2)2 D_Srv_ftm_stts_cn_center[1,1]
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
Code
lapply(models0, confint)
Output
$m0a
$m0a$`Surv(futime, status != "censored")`
2.5% 97.5%
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m1a
$m1a$`Surv(futime, status != "censored")`
2.5% 97.5%
age 0 0
sexfemale 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
2.5% 97.5%
age 0 0
sexfemale 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m2a
$m2a$`Surv(futime, status != "censored")`
2.5% 97.5%
copper 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m3a
$m3a$`Surv(futime, status != "censored")`
2.5% 97.5%
copper 0 0
sexfemale 0 0
age 0 0
abs(age - copper) 0 0
log(trig) 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m3b
$m3b$`Surv(futime, status != "censored")`
2.5% 97.5%
copper 0 0
sexfemale 0 0
age 0 0
abs(age - copper) 0 0
log(trig) 0 0
D_Srv_ftm_stts_cn_center[1,1] 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m4a
$m4a$`Surv(futime, status != "censored")`
2.5% 97.5%
age 0 0
sexfemale 0 0
trtplacebo 0 0
albumin 0 0
platelet 0 0
stage.L 0 0
stage.Q 0 0
stage.C 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m4b
$m4b$`Surv(futime, status != "censored")`
2.5% 97.5%
age 0 0
sexfemale 0 0
trtplacebo 0 0
sexfemale:trtplacebo 0 0
albumin 0 0
log(platelet) 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m4c
$m4c$`Surv(futime, status != "censored")`
2.5% 97.5%
age 0 0
sexfemale 0 0
albumin 0 0
log(platelet) 0 0
D_Srv_ftm_stts_cn_center[1,1] 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m4d
$m4d$`Surv(futime, status != "censored")`
2.5% 97.5%
age 0 0
sexfemale 0 0
albumin 0 0
ns(platelet, df = 2)1 0 0
ns(platelet, df = 2)2 0 0
D_Srv_ftm_stts_cn_center[1,1] 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
Code
lapply(models0, summary)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2,
n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 2:5
Sample size per chain = 4
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m1a
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:6
Sample size per chain = 4
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m1b
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:6
Sample size per chain = 4
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m2a
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:6
Sample size per chain = 4
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m3a
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m3b
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
Number of groups:
- center: 10
$m4a
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + trt + albumin + platelet + stage + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
trtplacebo 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
platelet 0 0 0 0 0 NaN NaN
stage.L 0 0 0 0 0 NaN NaN
stage.Q 0 0 0 0 0 NaN NaN
stage.C 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 2257
Number of groups:
- id: 312
$m4b
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex * trt + albumin + log(platelet) + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
trtplacebo 0 0 0 0 0 NaN NaN
sexfemale:trtplacebo 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
log(platelet) 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 2257
Number of groups:
- id: 312
$m4c
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + log(platelet) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
log(platelet) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 2257
Number of groups:
- center: 10
- id: 312
$m4d
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
ns(platelet, df = 2)1 0 0 0 0 0 NaN NaN
ns(platelet, df = 2)2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 2257
Number of groups:
- center: 10
- id: 312
Code
lapply(models0, function(x) coef(summary(x)))
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
$m0a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
$m1a
$m1a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
$m2a
$m2a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
$m3a
$m3a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
$m3b
$m3b$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
$m4a
$m4a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
trtplacebo 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
platelet 0 0 0 0 0 NaN NaN
stage.L 0 0 0 0 0 NaN NaN
stage.Q 0 0 0 0 0 NaN NaN
stage.C 0 0 0 0 0 NaN NaN
$m4b
$m4b$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
trtplacebo 0 0 0 0 0 NaN NaN
sexfemale:trtplacebo 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
log(platelet) 0 0 0 0 0 NaN NaN
$m4c
$m4c$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
log(platelet) 0 0 0 0 0 NaN NaN
$m4d
$m4d$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
ns(platelet, df = 2)1 0 0 0 0 0 NaN NaN
ns(platelet, df = 2)2 0 0 0 0 0 NaN NaN
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JointAI documentation built on April 27, 2023, 5:15 p.m.
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jagsmodel remains the same
Code
lapply(models, "[[", "jagsmodel")
Output
$m0a
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- 0
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
}
$m1a
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] +
M_lvlone[i, 5] * beta[2]
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
}
$m1b
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] +
M_lvlone[i, 5] * beta[2]
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:2) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
}
$m2a
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[1]
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:1) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
# Normal model for copper -------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- M_lvlone[i, 4] * alpha[1]
}
# Priors for the model for copper
for (k in 1:1) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
}
$m3a
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * beta[1] +
M_lvlone[i, 6] * beta[2] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * beta[3] +
(M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2] * beta[4] +
(M_lvlone[i, 9] - spM_lvlone[9, 1])/spM_lvlone[9, 2] * beta[5]
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
# Normal model for trig ---------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, )
mu_trig[i] <- M_lvlone[i, 5] * alpha[1] +
(M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] * alpha[2] +
M_lvlone[i, 6] * alpha[3] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[4]
M_lvlone[i, 9] <- log(M_lvlone[i, 3])
}
# Priors for the model for trig
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_trig <- sqrt(1/tau_trig)
# Normal model for copper -------------------------------------------------------
for (i in 1:312) {
M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- M_lvlone[i, 5] * alpha[5] + M_lvlone[i, 6] * alpha[6] +
(M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] * alpha[7]
M_lvlone[i, 8] <- abs(M_lvlone[i, 7] - M_lvlone[i, 4])
}
# Priors for the model for copper
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
}
$m3b
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (i in 1:312) {
logh0_Srv_ftm_stts_cn[i] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[i, ])
eta_Srv_ftm_stts_cn[i] <- b_Srv_ftm_stts_cn_center[group_center[i], 1] +
beta[1] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
beta[2] * M_lvlone[i, 5] +
beta[3] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2] +
beta[4] * (M_lvlone[i, 7] - spM_lvlone[7, 1])/spM_lvlone[7, 2] +
beta[5] * (M_lvlone[i, 8] - spM_lvlone[8, 1])/spM_lvlone[8, 2]
logh_Srv_ftm_stts_cn[i] <- logh0_Srv_ftm_stts_cn[i] + eta_Srv_ftm_stts_cn[i]
logh0s_Srv_ftm_stts_cn[i, 1:15] <- Bsh0_Srv_ftm_stts_cn[, i, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[i, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[i, ])
log.surv_Srv_ftm_stts_cn[i] <- -exp(eta_Srv_ftm_stts_cn[i]) * M_lvlone[i, 1]/2 * sum(Surv_Srv_ftm_stts_cn[i, ])
phi_Srv_ftm_stts_cn[i] <- 5000 - ((M_lvlone[i, 2] * logh_Srv_ftm_stts_cn[i])) - (log.surv_Srv_ftm_stts_cn[i])
zeros_Srv_ftm_stts_cn[i] ~ dpois(phi_Srv_ftm_stts_cn[i])
}
for (ii in 1:10) {
b_Srv_ftm_stts_cn_center[ii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[ii, ], invD_Srv_ftm_stts_cn_center[ , ])
mu_b_Srv_ftm_stts_cn_center[ii, 1] <- 0
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1])
# Normal mixed effects model for trig -------------------------------------------
for (i in 1:312) {
M_lvlone[i, 3] ~ dnorm(mu_trig[i], tau_trig)T(1e-04, )
mu_trig[i] <- b_trig_center[group_center[i], 1] +
alpha[2] * (M_lvlone[i, 4] - spM_lvlone[4, 1])/spM_lvlone[4, 2] +
alpha[3] * M_lvlone[i, 5] +
alpha[4] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 8] <- log(M_lvlone[i, 3])
}
for (ii in 1:10) {
b_trig_center[ii, 1:1] ~ dnorm(mu_b_trig_center[ii, ], invD_trig_center[ , ])
mu_b_trig_center[ii, 1] <- M_center[ii, 1] * alpha[1]
}
# Priors for the model for trig
for (k in 1:4) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_trig ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_trig <- sqrt(1/tau_trig)
invD_trig_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_trig_center[1, 1] <- 1 / (invD_trig_center[1, 1])
# Normal mixed effects model for copper -----------------------------------------
for (i in 1:312) {
M_lvlone[i, 4] ~ dnorm(mu_copper[i], tau_copper)
mu_copper[i] <- b_copper_center[group_center[i], 1] + alpha[6] * M_lvlone[i, 5] +
alpha[7] * (M_lvlone[i, 6] - spM_lvlone[6, 1])/spM_lvlone[6, 2]
M_lvlone[i, 7] <- abs(M_lvlone[i, 6] - M_lvlone[i, 4])
}
for (ii in 1:10) {
b_copper_center[ii, 1:1] ~ dnorm(mu_b_copper_center[ii, ], invD_copper_center[ , ])
mu_b_copper_center[ii, 1] <- M_center[ii, 1] * alpha[5]
}
# Priors for the model for copper
for (k in 5:7) {
alpha[k] ~ dnorm(mu_reg_norm, tau_reg_norm)
}
tau_copper ~ dgamma(shape_tau_norm, rate_tau_norm)
sigma_copper <- sqrt(1/tau_copper)
invD_copper_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_copper_center[1, 1] <- 1 / (invD_copper_center[1, 1])
}
$m4a
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (ii in 1:312) {
logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ])
eta_Srv_ftm_stts_cn[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[1] +
M_id[ii, 5] * beta[2] + M_id[ii, 6] * beta[3]
logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[4] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] +
M_lvlone[srow_Srv_ftm_stts_cn[ii], 3] * beta[6] +
M_lvlone[srow_Srv_ftm_stts_cn[ii], 4] * beta[7] +
M_lvlone[srow_Srv_ftm_stts_cn[ii], 5] * beta[8]
logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] +
(M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[4] +
(M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[5] +
M_lvlonegk[ii, 3, 1:15] * beta[6] +
M_lvlonegk[ii, 4, 1:15] * beta[7] +
M_lvlonegk[ii, 5, 1:15] * beta[8])
log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ])
phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii])
zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:8) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
}
$m4b
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (ii in 1:312) {
logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ])
eta_Srv_ftm_stts_cn[ii] <- (M_id[ii, 4] - spM_id[4, 1])/spM_id[4, 2] * beta[1] +
M_id[ii, 5] * beta[2] + M_id[ii, 6] * beta[3] +
M_id[ii, 7] * beta[4]
logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6]
logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] +
(M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[5] +
(M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[6])
log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ])
phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii])
zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii])
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:6) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
}
$m4c
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (ii in 1:312) {
logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ])
eta_Srv_ftm_stts_cn[ii] <- b_Srv_ftm_stts_cn_center[group_center[pos_id[ii]], 1] +
beta[1] * (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] +
beta[2] * M_id[ii, 4]
logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4]
logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] +
(M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] +
(M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4])
log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ])
phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii])
zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii])
}
for (iii in 1:10) {
b_Srv_ftm_stts_cn_center[iii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[iii, ], invD_Srv_ftm_stts_cn_center[ , ])
mu_b_Srv_ftm_stts_cn_center[iii, 1] <- 0
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:4) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1])
}
$m4d
model {
# Cox PH model for Srv_ftm_stts_cn ----------------------------------------------
for (ii in 1:312) {
logh0_Srv_ftm_stts_cn[ii] <- inprod(beta_Bh0_Srv_ftm_stts_cn[], Bh0_Srv_ftm_stts_cn[ii, ])
eta_Srv_ftm_stts_cn[ii] <- b_Srv_ftm_stts_cn_center[group_center[pos_id[ii]], 1] +
beta[1] * (M_id[ii, 3] - spM_id[3, 1])/spM_id[3, 2] +
beta[2] * M_id[ii, 4]
logh_Srv_ftm_stts_cn[ii] <- logh0_Srv_ftm_stts_cn[ii] + eta_Srv_ftm_stts_cn[ii] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 1] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 2] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] +
(M_lvlone[srow_Srv_ftm_stts_cn[ii], 3] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5]
logh0s_Srv_ftm_stts_cn[ii, 1:15] <- Bsh0_Srv_ftm_stts_cn[, ii, ] %*% beta_Bh0_Srv_ftm_stts_cn[]
Surv_Srv_ftm_stts_cn[ii, 1:15] <- gkw[] * exp(1)^(logh0s_Srv_ftm_stts_cn[ii, ] +
(M_lvlonegk[ii, 1, 1:15] - spM_lvlone[1, 1])/spM_lvlone[1, 2] * beta[3] +
(M_lvlonegk[ii, 2, 1:15] - spM_lvlone[2, 1])/spM_lvlone[2, 2] * beta[4] +
(M_lvlonegk[ii, 3, 1:15] - spM_lvlone[3, 1])/spM_lvlone[3, 2] * beta[5])
log.surv_Srv_ftm_stts_cn[ii] <- -exp(eta_Srv_ftm_stts_cn[ii]) * M_id[ii, 1]/2 * sum(Surv_Srv_ftm_stts_cn[ii, ])
phi_Srv_ftm_stts_cn[ii] <- 5000 - ((M_id[ii, 2] * logh_Srv_ftm_stts_cn[ii])) - (log.surv_Srv_ftm_stts_cn[ii])
zeros_Srv_ftm_stts_cn[ii] ~ dpois(phi_Srv_ftm_stts_cn[ii])
}
for (iii in 1:10) {
b_Srv_ftm_stts_cn_center[iii, 1:1] ~ dnorm(mu_b_Srv_ftm_stts_cn_center[iii, ], invD_Srv_ftm_stts_cn_center[ , ])
mu_b_Srv_ftm_stts_cn_center[iii, 1] <- 0
}
# Priors for the coefficients in the model for Srv_ftm_stts_cn
for (k in 1:5) {
beta[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
for (k in 1:6) {
beta_Bh0_Srv_ftm_stts_cn[k] ~ dnorm(mu_reg_surv, tau_reg_surv)
}
invD_Srv_ftm_stts_cn_center[1, 1] ~ dgamma(shape_diag_RinvD, rate_diag_RinvD)T(1e-16, 1e16)
D_Srv_ftm_stts_cn_center[1, 1] <- 1 / (invD_Srv_ftm_stts_cn_center[1, 1])
}
GRcrit and MCerror give same result
Code
lapply(models0, GR_crit, multivariate = FALSE)
Output
$m0a
Potential scale reduction factors:
Point est. Upper C.I.
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m1a
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m1b
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m2a
Potential scale reduction factors:
Point est. Upper C.I.
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
copper NaN NaN
$m3a
Potential scale reduction factors:
Point est. Upper C.I.
copper NaN NaN
sexfemale NaN NaN
age NaN NaN
abs(age - copper) NaN NaN
log(trig) NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m3b
Potential scale reduction factors:
Point est. Upper C.I.
copper NaN NaN
sexfemale NaN NaN
age NaN NaN
abs(age - copper) NaN NaN
log(trig) NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
D_Srv_ftm_stts_cn_center[1,1] NaN NaN
$m4a
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
trtplacebo NaN NaN
albumin NaN NaN
platelet NaN NaN
stage.L NaN NaN
stage.Q NaN NaN
stage.C NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m4b
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
trtplacebo NaN NaN
sexfemale:trtplacebo NaN NaN
albumin NaN NaN
log(platelet) NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
$m4c
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
albumin NaN NaN
log(platelet) NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
D_Srv_ftm_stts_cn_center[1,1] NaN NaN
$m4d
Potential scale reduction factors:
Point est. Upper C.I.
age NaN NaN
sexfemale NaN NaN
albumin NaN NaN
ns(platelet, df = 2)1 NaN NaN
ns(platelet, df = 2)2 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[1] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] NaN NaN
D_Srv_ftm_stts_cn_center[1,1] NaN NaN
Code
lapply(models0, MC_error)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
est MCSE SD MCSE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m1a
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m1b
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m2a
est MCSE SD MCSE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
copper 0 0 0 NaN
$m3a
est MCSE SD MCSE/SD
copper 0 0 0 NaN
sexfemale 0 0 0 NaN
age 0 0 0 NaN
abs(age - copper) 0 0 0 NaN
log(trig) 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m3b
est MCSE SD MCSE/SD
copper 0 0 0 NaN
sexfemale 0 0 0 NaN
age 0 0 0 NaN
abs(age - copper) 0 0 0 NaN
log(trig) 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN
$m4a
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
trtplacebo 0 0 0 NaN
albumin 0 0 0 NaN
platelet 0 0 0 NaN
stage.L 0 0 0 NaN
stage.Q 0 0 0 NaN
stage.C 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m4b
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
trtplacebo 0 0 0 NaN
sexfemale:trtplacebo 0 0 0 NaN
albumin 0 0 0 NaN
log(platelet) 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
$m4c
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
albumin 0 0 0 NaN
log(platelet) 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN
$m4d
est MCSE SD MCSE/SD
age 0 0 0 NaN
sexfemale 0 0 0 NaN
albumin 0 0 0 NaN
ns(platelet, df = 2)1 0 0 0 NaN
ns(platelet, df = 2)2 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 NaN
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 NaN
summary output remained the same
Code
lapply(models0, print)
Output
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2,
n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale
0 0
Call:
coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, I(status != "censored"))"
Coefficients:
age sexfemale
0 0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper
0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper sexfemale age abs(age - copper)
0 0 0 0
log(trig)
0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper sexfemale age abs(age - copper)
0 0 0 0
log(trig)
0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + trt + albumin + platelet + stage + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale trtplacebo albumin platelet stage.L stage.Q
0 0 0 0 0 0 0
stage.C
0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex * trt + albumin + log(platelet) + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale trtplacebo
0 0 0
sexfemale:trtplacebo albumin log(platelet)
0 0 0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + log(platelet) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale albumin log(platelet)
0 0 0 0
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale albumin
0 0 0
ns(platelet, df = 2)1 ns(platelet, df = 2)2
0 0
$m0a
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2,
n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
$m1a
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale
0 0
$m1b
Call:
coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, I(status != "censored"))"
Coefficients:
age sexfemale
0 0
$m2a
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper
0
$m3a
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper sexfemale age abs(age - copper)
0 0 0 0
log(trig)
0
$m3b
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
copper sexfemale age abs(age - copper)
0 0 0 0
log(trig)
0
$m4a
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + trt + albumin + platelet + stage + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale trtplacebo albumin platelet stage.L stage.Q
0 0 0 0 0 0 0
stage.C
0
$m4b
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex * trt + albumin + log(platelet) + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale trtplacebo
0 0 0
sexfemale:trtplacebo albumin log(platelet)
0 0 0
$m4c
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + log(platelet) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale albumin log(platelet)
0 0 0 0
$m4d
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Bayesian proportional hazards model for "Surv(futime, status != "censored")"
Coefficients:
age sexfemale albumin
0 0 0
ns(platelet, df = 2)1 ns(platelet, df = 2)2
0 0
Code
lapply(models0, coef)
Output
$m0a
$m0a$`Surv(futime, status != "censored")`
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m1a
$m1a$`Surv(futime, status != "censored")`
age sexfemale
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
age sexfemale
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m2a
$m2a$`Surv(futime, status != "censored")`
copper beta_Bh0_Srv_ftm_stts_cn[1]
0 0
beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3]
0 0
beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5]
0 0
beta_Bh0_Srv_ftm_stts_cn[6]
0
$m3a
$m3a$`Surv(futime, status != "censored")`
copper sexfemale
0 0
age abs(age - copper)
0 0
log(trig) beta_Bh0_Srv_ftm_stts_cn[1]
0 0
beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3]
0 0
beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5]
0 0
beta_Bh0_Srv_ftm_stts_cn[6]
0
$m3b
$m3b$`Surv(futime, status != "censored")`
copper sexfemale
0 0
age abs(age - copper)
0 0
log(trig) D_Srv_ftm_stts_cn_center[1,1]
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m4a
$m4a$`Surv(futime, status != "censored")`
age sexfemale
0 0
trtplacebo albumin
0 0
platelet stage.L
0 0
stage.Q stage.C
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m4b
$m4b$`Surv(futime, status != "censored")`
age sexfemale
0 0
trtplacebo sexfemale:trtplacebo
0 0
albumin log(platelet)
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
$m4c
$m4c$`Surv(futime, status != "censored")`
age sexfemale
0 0
albumin log(platelet)
0 0
D_Srv_ftm_stts_cn_center[1,1] beta_Bh0_Srv_ftm_stts_cn[1]
0 0
beta_Bh0_Srv_ftm_stts_cn[2] beta_Bh0_Srv_ftm_stts_cn[3]
0 0
beta_Bh0_Srv_ftm_stts_cn[4] beta_Bh0_Srv_ftm_stts_cn[5]
0 0
beta_Bh0_Srv_ftm_stts_cn[6]
0
$m4d
$m4d$`Surv(futime, status != "censored")`
age sexfemale
0 0
albumin ns(platelet, df = 2)1
0 0
ns(platelet, df = 2)2 D_Srv_ftm_stts_cn_center[1,1]
0 0
beta_Bh0_Srv_ftm_stts_cn[1] beta_Bh0_Srv_ftm_stts_cn[2]
0 0
beta_Bh0_Srv_ftm_stts_cn[3] beta_Bh0_Srv_ftm_stts_cn[4]
0 0
beta_Bh0_Srv_ftm_stts_cn[5] beta_Bh0_Srv_ftm_stts_cn[6]
0 0
Code
lapply(models0, confint)
Output
$m0a
$m0a$`Surv(futime, status != "censored")`
2.5% 97.5%
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m1a
$m1a$`Surv(futime, status != "censored")`
2.5% 97.5%
age 0 0
sexfemale 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
2.5% 97.5%
age 0 0
sexfemale 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m2a
$m2a$`Surv(futime, status != "censored")`
2.5% 97.5%
copper 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m3a
$m3a$`Surv(futime, status != "censored")`
2.5% 97.5%
copper 0 0
sexfemale 0 0
age 0 0
abs(age - copper) 0 0
log(trig) 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m3b
$m3b$`Surv(futime, status != "censored")`
2.5% 97.5%
copper 0 0
sexfemale 0 0
age 0 0
abs(age - copper) 0 0
log(trig) 0 0
D_Srv_ftm_stts_cn_center[1,1] 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m4a
$m4a$`Surv(futime, status != "censored")`
2.5% 97.5%
age 0 0
sexfemale 0 0
trtplacebo 0 0
albumin 0 0
platelet 0 0
stage.L 0 0
stage.Q 0 0
stage.C 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m4b
$m4b$`Surv(futime, status != "censored")`
2.5% 97.5%
age 0 0
sexfemale 0 0
trtplacebo 0 0
sexfemale:trtplacebo 0 0
albumin 0 0
log(platelet) 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m4c
$m4c$`Surv(futime, status != "censored")`
2.5% 97.5%
age 0 0
sexfemale 0 0
albumin 0 0
log(platelet) 0 0
D_Srv_ftm_stts_cn_center[1,1] 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
$m4d
$m4d$`Surv(futime, status != "censored")`
2.5% 97.5%
age 0 0
sexfemale 0 0
albumin 0 0
ns(platelet, df = 2)1 0 0
ns(platelet, df = 2)2 0 0
D_Srv_ftm_stts_cn_center[1,1] 0 0
beta_Bh0_Srv_ftm_stts_cn[1] 0 0
beta_Bh0_Srv_ftm_stts_cn[2] 0 0
beta_Bh0_Srv_ftm_stts_cn[3] 0 0
beta_Bh0_Srv_ftm_stts_cn[4] 0 0
beta_Bh0_Srv_ftm_stts_cn[5] 0 0
beta_Bh0_Srv_ftm_stts_cn[6] 0 0
Code
lapply(models0, summary)
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2,
n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 2:5
Sample size per chain = 4
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m1a
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:6
Sample size per chain = 4
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m1b
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age +
sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:6
Sample size per chain = 4
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m2a
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper,
data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE,
mess = FALSE)
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:6
Sample size per chain = 4
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m3a
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2,
n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04,
NA)))
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
$m3b
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper +
sex + age + abs(age - copper) + log(trig) + (1 | center),
data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, trunc = list(trig = c(1e-04, NA)))
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 312
Number of groups:
- center: 10
$m4a
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + trt + albumin + platelet + stage + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
trtplacebo 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
platelet 0 0 0 0 0 NaN NaN
stage.L 0 0 0 0 0 NaN NaN
stage.Q 0 0 0 0 0 NaN NaN
stage.C 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 2257
Number of groups:
- id: 312
$m4b
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex * trt + albumin + log(platelet) + (1 | id), data = PBC,
n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE,
timevar = "day")
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
trtplacebo 0 0 0 0 0 NaN NaN
sexfemale:trtplacebo 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
log(platelet) 0 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 2257
Number of groups:
- id: 312
$m4c
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + log(platelet) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
log(platelet) 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 2257
Number of groups:
- center: 10
- id: 312
$m4d
Bayesian proportional hazards model fitted with JointAI
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age +
sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center),
data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE,
mess = FALSE, timevar = "day")
Number of events: 169
Posterior summary:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
ns(platelet, df = 2)1 0 0 0 0 0 NaN NaN
ns(platelet, df = 2)2 0 0 0 0 0 NaN NaN
Posterior summary of random effects covariance matrix:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1] 0 0 0 0 NaN NaN
Posterior summary of other parameters:
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[2] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[3] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[4] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[5] 0 0 0 0 0 NaN NaN
beta_Bh0_Srv_ftm_stts_cn[6] 0 0 0 0 0 NaN NaN
MCMC settings:
Iterations = 3:12
Sample size per chain = 10
Thinning interval = 1
Number of chains = 3
Number of observations: 2257
Number of groups:
- center: 10
- id: 312
Code
lapply(models0, function(x) coef(summary(x)))
Output
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a
$m0a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
$m1a
$m1a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
$m1b
$m1b$`Surv(futime, I(status != "censored"))`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
$m2a
$m2a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
$m3a
$m3a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
$m3b
$m3b$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
age 0 0 0 0 0 NaN NaN
abs(age - copper) 0 0 0 0 0 NaN NaN
log(trig) 0 0 0 0 0 NaN NaN
$m4a
$m4a$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
trtplacebo 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
platelet 0 0 0 0 0 NaN NaN
stage.L 0 0 0 0 0 NaN NaN
stage.Q 0 0 0 0 0 NaN NaN
stage.C 0 0 0 0 0 NaN NaN
$m4b
$m4b$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
trtplacebo 0 0 0 0 0 NaN NaN
sexfemale:trtplacebo 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
log(platelet) 0 0 0 0 0 NaN NaN
$m4c
$m4c$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
log(platelet) 0 0 0 0 0 NaN NaN
$m4d
$m4d$`Surv(futime, status != "censored")`
Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age 0 0 0 0 0 NaN NaN
sexfemale 0 0 0 0 0 NaN NaN
albumin 0 0 0 0 0 NaN NaN
ns(platelet, df = 2)1 0 0 0 0 0 NaN NaN
ns(platelet, df = 2)2 0 0 0 0 0 NaN NaN
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