fit_spm_jags: Fit SPM with MNAR monotone dropout in JAGS (continuous or...

In BDIB: Bayesian Dynamic Information Borrowing for Longitudinal Data with Informative Dropout

Fit SPM with MNAR monotone dropout in JAGS (continuous or binary outcomes)

Description

Unified JAGS interface for the shared-parameter model (SPM) with MNAR dropout:

• family="gaussian": Continuous outcome.

• family="bernoulli": Binary outcome.

Usage

fit_spm_jags(
  df,
  family = c("gaussian", "bernoulli"),
  prior_theta = NULL,
  params_to_save = NULL,
  n.chains = 3,
  n.iter = 10000,
  n.burnin = 5000,
  n.thin = 2,
  parallel = FALSE
)

Arguments

df	Long-format data.frame with columns id, time, R, Y. R=1 means observed, R=0 means missing.
family	Either "gaussian" or "bernoulli" (probit).
prior_theta	Optional prior for \theta. family="gaussian": RBesT::normMix object (mixture Normal on \theta). family="bernoulli": a "betaMix" object (mixture Beta on \theta). If NULL, defaults to a very weak one-component prior.
params_to_save	Character vector of parameter names to monitor. If NULL, sensible defaults are used for each family.
n.chains, n.iter, n.burnin, n.thin	MCMC settings passed to jagsUI::jags.
parallel	Logical; whether to run chains in parallel (via jagsUI).

Value

Numeric vector of posterior draws of \theta.

Examples

# ---- Example 1: Continuous outcome with weak prior ----
set.seed(1000)

# 1) Generate longitudinal data with MNAR monotone dropout
t_vec <- seq(4, 48, 4)
gen <- generate_spm(
  n = 80, t_vec = t_vec, family = "gaussian",
  alpha0 = -4, alpha1 = 0.05, rho = 1, sigma_b = 1,
  theta = -3.2,   # total change at the last visit (= slope * max(t))
  sigma_e = 1
)
df <- gen$dat  # long-format data: id, time, R, Y

# 2) Define a weakly informative prior for theta (one Normal component with large variance)
prior <- RBesT::mixnorm(c(1, 0, 1000))  # weight=1, mean=0, sd=1000

# 3) Fit the SPM (MNAR) and obtain posterior draws of theta

# Full MCMC fitting takes longer than a brief example check.
draws_theta <- fit_spm_jags(
  df = df, family = "gaussian",
  prior_theta = prior,
  params_to_save = c("theta","slope","alpha0","alpha1","rho","sigma_e","sigma_b"),
  n.chains = 3, n.iter = 10000, n.burnin = 5000, n.thin = 2, parallel = FALSE
)

# Summarize posterior results
summary(draws_theta)    # posterior mean, median, quantiles
mean(draws_theta)       # posterior mean of theta


# ---- Example 2: Binary outcome (probit link) with weak prior ----
set.seed(1000)
# 1) Generate longitudinal binary data with MNAR monotone dropout
t_vec <- seq(4, 48, 4)
gen <- generate_spm(
  n       = 200,
  t_vec   = t_vec,
  family  = "bernoulli",
  alpha0  = -4,       # dropout logit-intercept
  alpha1  =  0.05,    # dropout logit-slope in time
  rho     = -1,       # association between random effect and dropout
  sigma_b =  1,       # SD of random intercept b_i
  theta   =  0.4,    # target marginal Pr(Y=1) at final visit
  beta0   = -2        # outcome probit-intercept
)
df <- gen$dat  # long-format data: id, time, R, Y

# 2) Define a weakly informative prior for theta:
#    Beta(1,1) (Uniform on (0,1)) as a one-component mixture
prior <- RBesT::mixbeta(c(1, 1, 1))  # weight=1, a=1, b=1

# 3) Fit the SPM (MNAR) with probit link for the binary outcome
#    and obtain posterior draws of theta (final-visit response rate)

# Full MCMC fitting takes longer than a brief example check.
draws_theta <- fit_spm_jags(
  df             = df,
  family         = "bernoulli",
  prior_theta    = prior,
  params_to_save = c("theta","slope","beta0","alpha0","alpha1","rho","sigma_b"),
  n.chains       = 3,
  n.iter         = 5000,
  n.burnin       = 3000,
  n.thin         = 2,
  parallel       = FALSE
)

# 4) Summarize posterior results
summary(draws_theta)   # posterior mean, median, quantiles of theta
mean(draws_theta)      # posterior mean of theta

BDIB documentation built on Oct. 18, 2026, 5:08 p.m.