Nothing
BimixedContCont <- function(Dataset, Surr, True, Treat, Trial.ID, Pat.ID, Model=c("Full"),
Min.Trial.Size=2, Alpha=.05,
T0T1=seq(-1, 1, by=.2), T0S1=seq(-1, 1, by=.2), T1S0=seq(-1, 1, by=.2),
S0S1=seq(-1, 1, by=.2), ...){
if ((Model==c("Full") | Model==c("Reduced"))==FALSE){stop ("The specification of the Model=c(\"...\") argument of the call is incorrect. Use either Model=c(\"Full\") or Model=c(\"Reduced\").")}
Surr <- Dataset[,paste(substitute(Surr))]
True <- Dataset[,paste(substitute(True))]
Treat <- Dataset[,paste(substitute(Treat))]
Trial.ID <- Dataset[,paste(substitute(Trial.ID))]
Pat.ID <- Dataset[,paste(substitute(Pat.ID))]
# Call Data.Processing
Data.Proc <- .Data.Processing(Dataset=Dataset, Surr=Surr, True=True, Treat=Treat, Trial.ID=Trial.ID, Pat.ID=Pat.ID, Min.Trial.Size=Min.Trial.Size)
wide <- Data.Proc$wide
dataS <- Data.Proc$dataS
dataT <- Data.Proc$dataT
Data.analyze <- Data.Proc$Data.analyze
N.total <- Data.Proc$N.total
N.trial <- Data.Proc$N.trial
Obs.per.trial <- Data.Proc$Obs.per.trial
# ICA
S1 <- dataS$outcome[dataS$Treat==1]
S0 <- dataS$outcome[dataS$Treat!=1]
T1 <- dataT$outcome[dataS$Treat==1]
T0 <- dataT$outcome[dataS$Treat!=1]
r_T0S0 <- cor(T0,S0)
r_T1S1 <- cor(T1,S1)
set.seed(123); ICA <- ICA.ContCont(T0S0 = r_T0S0, T1S1 = r_T1S1,
T0T0 = var(T0), T1T1 = var(T1), S0S0 = var(S0), S1S1 = var(S1),
T0T1=T0T1, T0S1=T0S1, T1S0=T1S0, S0S1=S0S1)
if (Model==c("Full")){
Model.S.and.T <- lmer(outcome ~ -1 + as.factor(endpoint):Treat + as.factor(endpoint) +
(-1+endpoint+endpoint:Treat|Trial.ID), data=Data.analyze, ...) #
Fixed.effect.pars.S <- rbind(summary(Model.S.and.T)$coefficients[1], summary(Model.S.and.T)$coefficients[3])
Fixed.effect.pars.T <- rbind(summary(Model.S.and.T)$coefficients[2], summary(Model.S.and.T)$coefficients[4])
Fixed.Effect.Pars <- data.frame(rbind(Fixed.effect.pars.S, Fixed.effect.pars.T), stringsAsFactors = TRUE)
rownames(Fixed.Effect.Pars) <- c("Intercept.S", "Treatment.S", "Intercept.T", "Treatment.T")
colnames(Fixed.Effect.Pars) <- c(" ")
Random.effect.pars.S <- cbind(data.frame(ranef(Model.S.and.T)$Trial.ID, stringsAsFactors = TRUE)[,1],
data.frame(ranef(Model.S.and.T)$Trial.ID, stringsAsFactors = TRUE)[,3])
Random.effect.pars.T <- cbind(data.frame(ranef(Model.S.and.T)$Trial.ID, stringsAsFactors = TRUE)[,2],
data.frame(ranef(Model.S.and.T)$Trial.ID, stringsAsFactors = TRUE)[,4])
Random.Effect.Pars <- data.frame(cbind(Random.effect.pars.S, Random.effect.pars.T), stringsAsFactors = TRUE)
colnames(Random.Effect.Pars) <- c("Intercept.S", "Treatment.S", "Intercept.T", "Treatment.T")
Residuals.S.and.T <- residuals(Model.S.and.T, type='response')
Residuals.S <- Residuals.S.and.T[seq(from=1, to=length(Residuals.S.and.T), by=2)]
names(Residuals.S) <- NULL
Residuals.T <- Residuals.S.and.T[seq(from=2, to=length(Residuals.S.and.T), by=2)]
names(Residuals.T) <- NULL
Residuals <- data.frame(cbind(wide$Pat.ID, Residuals.S, Residuals.T), stringsAsFactors = TRUE)
colnames(Residuals) <- c("Pat.ID", "Residuals.S", "Residuals.T")
rownames(Residuals) <- NULL
Residuals <- Residuals[order(Residuals$Pat.ID),]
Intercept.S <- coef(Model.S.and.T)$Trial.ID[,1]+coef(Model.S.and.T)$Trial.ID[,5]
Intercept.T <- coef(Model.S.and.T)$Trial.ID[,2]+coef(Model.S.and.T)$Trial.ID[,6]
Treatment.S <- coef(Model.S.and.T)$Trial.ID[,3]+coef(Model.S.and.T)$Trial.ID[,7]
Treatment.T <- coef(Model.S.and.T)$Trial.ID[,4]+coef(Model.S.and.T)$Trial.ID[,8]
Trial.Spec.Results <- cbind(Obs.per.trial$Trial, Obs.per.trial$Obs.per.trial, Intercept.S, Intercept.T, Treatment.S, Treatment.T)
colnames(Trial.Spec.Results) <- c(NULL, "Trial", "Obs.per.trial", "Intercept.S", "Intercept.T", "Treatment.S", "Treatment.T")
rownames(Trial.Spec.Results) <- NULL
}
if (Model==c("Reduced")){
Model.S.and.T <- lmer(outcome ~ -1 + as.factor(endpoint):Treat + as.factor(endpoint) + (-1+endpoint:Treat|Trial.ID), data=Data.analyze, ...) #
Fixed.effect.pars.S <- rbind(summary(Model.S.and.T)$coefficients[1], summary(Model.S.and.T)$coefficients[3])
Fixed.effect.pars.T <- rbind(summary(Model.S.and.T)$coefficients[2], summary(Model.S.and.T)$coefficients[4])
Fixed.Effect.Pars <- rbind(Fixed.effect.pars.S, Fixed.effect.pars.T)
rownames(Fixed.Effect.Pars) <- c("Intercept.S", "Treatment.S", "Intercept.T", "Treatment.T")
colnames(Fixed.Effect.Pars) <- c(" ")
Random.Effect.Pars <- data.frame(ranef(Model.S.and.T)$Trial.ID, stringsAsFactors = TRUE)
colnames(Random.Effect.Pars) <- c("Treatment.S", "Treatment.T")
Residuals.S.and.T <- residuals(Model.S.and.T, type='response')
Residuals.S <- Residuals.S.and.T[seq(from=1, to=length(Residuals.S.and.T), by=2)]
names(Residuals.S) <- NULL
Residuals.T <- Residuals.S.and.T[seq(from=2, to=length(Residuals.S.and.T), by=2)]
names(Residuals.T) <- NULL
Residuals <- data.frame(cbind(wide$Pat.ID, Residuals.S, Residuals.T), stringsAsFactors = TRUE)
colnames(Residuals) <- c("Pat.ID", "Residuals.S", "Residuals.T")
rownames(Residuals) <- NULL
Residuals <- Residuals[order(Residuals$Pat.ID),]
Treatment.S <- coef(Model.S.and.T)$Trial.ID[,1]+coef(Model.S.and.T)$Trial.ID[,5]
Treatment.T <- coef(Model.S.and.T)$Trial.ID[,2]+coef(Model.S.and.T)$Trial.ID[,6]
Trial.Spec.Results <- cbind(Obs.per.trial$Trial, Obs.per.trial$Obs.per.trial, Treatment.S, Treatment.T)
colnames(Trial.Spec.Results) <- c(NULL, "Trial", "Obs.per.trial", "Treatment.S", "Treatment.T")
rownames(Trial.Spec.Results) <- NULL
}
# Trial-level surrogacy estimates
if (Model==c("Full")){
D <- matrix(summary(Model.S.and.T)$varcor$Trial.ID[1:16], ncol=4)
rownames(D) <- colnames(D) <- c("Intercept.S", "Intercept.T", "Treatment.S", "Treatment.T")
Min.Eigen.D <- min(eigen(D)$values)
if (Min.Eigen.D <= 0) warning(paste("The R-square Trial estimate may be invalid, because its calculation is based on a non-positive definite covariance matrix"))
singular <- svd(D)$d
Cond.Number.D.Matrix <- max(singular)/min(singular)
if (Cond.Number.D.Matrix > 100) warning(paste("The R-square Trial estimate may not be thrustworthy, as the conditioning number of the D matrix is high and equals",
deparse(Cond.Number.D.Matrix)))
A <- matrix(c(D[4,1], D[4,3]), nrow=2, ncol=1)
B <- matrix(c(D[1,1], D[3,1], D[3,1], D[3,3]), nrow=2, ncol=2)
C <- as.matrix(D[4,4])
Trial.R2.value <- (t(A) %*% solve(B) %*% A)/C
}
if (Model==c("Reduced")){
D <- matrix(summary(Model.S.and.T)$varcor$Trial.ID[1:4], ncol=2)
rownames(D) <- colnames(D) <- c("Treatment.S", "Treatment.T")
Min.Eigen.D <- min(eigen(D)$values)
if (Min.Eigen.D <= 0) warning(paste("The R-square Trial estimate may be invalid, because its calculation is based on a non-positive definite covariance matrix"))
singular <- svd(D)$d
Cond.Number.D.Matrix <- max(singular)/min(singular)
if (Cond.Number.D.Matrix > 100) warning(paste("The R-square Trial estimate may not be thrustworthy, as the conditioning number of the D matrix is high and equals",
deparse(Cond.Number.D.Matrix)))
Trial.R2.value <- (D[2,1]**2)/(D[1,1]*D[2,2])
}
# R2 trial
Trial.R2.sd <- sqrt((4*Trial.R2.value*(1-Trial.R2.value)^2)/(N.trial-3))
Trial.R2.lb <- max(0, Trial.R2.value + qnorm(Alpha/2) *(Trial.R2.sd))
Trial.R2.ub <- min(1, Trial.R2.value + qnorm(1-Alpha/2)*(Trial.R2.sd))
Trial.R2 <- data.frame(cbind(Trial.R2.value, Trial.R2.sd, Trial.R2.lb, Trial.R2.ub), stringsAsFactors = TRUE)
colnames(Trial.R2) <- c("R2 Trial", "Standard Error", "CI lower limit", "CI upper limit")
rownames(Trial.R2) <- c(" ")
# Rtrial
Trial.R.value <- sqrt(Trial.R2.value)
Z <- .5*log((1+Trial.R.value)/(1-Trial.R.value))
Trial.R.lb <- max(0, (exp(2*(Z-(qnorm(1-Alpha/2)*sqrt(1/(N.trial-3)))))-1)/(exp(2*(Z-(qnorm(1-Alpha/2)*sqrt(1/(N.trial-3)))))+1))
Trial.R.ub <- min(1, (exp(2*(Z+(qnorm(1-Alpha/2)*sqrt(1/(N.trial-3)))))-1)/(exp(2*(Z+(qnorm(1-Alpha/2)*sqrt(1/(N.trial-3)))))+1))
Trial.R.sd <- sqrt((1-Trial.R.value**2)/(N.trial-2))
Trial.R <- data.frame(cbind(Trial.R.value, Trial.R.sd, Trial.R.lb, Trial.R.ub), stringsAsFactors = TRUE)
colnames(Trial.R) <- c("R Trial", "Standard Error", "CI lower limit", "CI upper limit")
rownames(Trial.R) <- c(" ")
# R2 indiv
if (Model==c("Full")){
rm(Model.S.and.T)
Model.S.and.T <- lme(outcome~ -1 + as.factor(endpoint):Treat + as.factor(endpoint),
random=~ -1 + as.factor(endpoint) + as.factor(endpoint):Treat|as.factor(Trial.ID),
correlation = nlme::corSymm(form=~1|as.factor(Trial.ID)/as.factor(Pat.ID)), data=Data.analyze,
weights=nlme::varIdent(form=~1|endpoint), control = list(msVerbose = FALSE, optimizer = "nlm", niterEM = 25, msMaxIter=500))
}
if (Model==c("Reduced")){
rm(Model.S.and.T)
Model.S.and.T <- lme(outcome~ -1 + as.factor(endpoint):Treat + as.factor(endpoint),
random=~ -1 + as.factor(endpoint):Treat|as.factor(Trial.ID),
correlation = nlme::corSymm(form=~1|as.factor(Trial.ID)/as.factor(Pat.ID)), data=Data.analyze,
weights=nlme::varIdent(form=~1|endpoint), control = list(msVerbose = FALSE, optimizer = "nlm", niterEM = 25, msMaxIter=500))
}
cors <- nlme::corMatrix(Model.S.and.T$modelStruct$corStruct)[[1]]
varStruct <- capture.output(Model.S.and.T$modelStruct$varStruct)[3]
varStruct <- cbind(as.numeric(unique(strsplit(varStruct, " ")[[1]])[1]), as.numeric(unique(strsplit(varStruct, " ")[[1]])[2]))
vars <- as.numeric((varStruct**2) * (summary(Model.S.and.T)$sigma)**2)
covs <- outer(vars, vars, function(x,y) sqrt(x)*sqrt(y))
VarCovarResid <- cors * covs
rownames(VarCovarResid) <- colnames(VarCovarResid) <- c("Surr", "True")
R2ind <- (VarCovarResid[2,1]**2)/(VarCovarResid[1,1]*VarCovarResid[2,2])
R2ind.sd <- sqrt((4*R2ind*((1-R2ind)**2))/(N.total-3))
R2ind.lb <- max(0,R2ind + qnorm(Alpha/2)*R2ind.sd)
R2ind.ub <- min(1,R2ind + qnorm(1-Alpha/2)*R2ind.sd)
Indiv.R2 <- data.frame(cbind(R2ind, R2ind.sd, R2ind.lb, R2ind.ub), stringsAsFactors = TRUE)
colnames(Indiv.R2) <- c("R2 Indiv", "Standard Error", "CI lower limit", "CI upper limit")
rownames(Indiv.R2) <- c(" ")
# R ind
Rind <- sqrt((VarCovarResid[2,1]**2)/(VarCovarResid[1,1]*VarCovarResid[2,2]))
Z <- .5*log((1+Rind)/(1-Rind))
Indiv.R.lb <- max(0, (exp(2*(Z-(qnorm(1-.05/2)*sqrt(1/(N.trial-3)))))-1)/(exp(2*(Z-(qnorm(1-.05/2)*sqrt(1/(N.trial-3)))))+1))
Indiv.R.ub <- min(1, (exp(2*(Z+(qnorm(1-.05/2)*sqrt(1/(N.trial-3)))))-1)/(exp(2*(Z+(qnorm(1-.05/2)*sqrt(1/(N.trial-3)))))+1))
Indiv.R.sd <- sqrt((1-Rind**2)/(N.total-2))
Indiv.R <- data.frame(cbind(Rind, Indiv.R.sd, Indiv.R.lb, Indiv.R.ub), stringsAsFactors = TRUE)
colnames(Indiv.R) <- c("R Indiv", "Standard Error", "CI lower limit", "CI upper limit")
row.names(Indiv.R) <- c(" ")
Min.Eigen.VarCovarResid <- min(eigen(VarCovarResid)$values) # lowest eigenvalue
if (Min.Eigen.VarCovarResid <= 0) warning(paste("The R-square Individual estimate may be invalid, because its calculation is based on a non-positive definite covariance matrix"))
Singular <- svd(VarCovarResid)$d
Cond.Number.VarCovarResid <- max(Singular)/min(Singular)
if (Cond.Number.VarCovarResid > 100) warning(paste("The R-square Individual estimate may not be thrustworthy, as the conditioning number of the Sigma matrix is high and equals",
deparse(Cond.Number.VarCovarResid)))
NoTreat <- wide[wide$Treat!=1,]
Treat <- wide[wide$Treat==1,]
T0S0 <- cor(NoTreat$Surr, NoTreat$True)
T1S1 <- cor(Treat$Surr, Treat$True)
Z_T0S0 <- .5*log((1+T0S0)/(1-T0S0))
rho_lb <- max(0, (exp(2*(Z_T0S0-(qnorm(1-Alpha/2)*sqrt(1/(N.total-3)))))-1)/(exp(2*(Z_T0S0-(qnorm(1-Alpha/2)*sqrt(1/(N.total-3)))))+1))
rho_ub <- min(1, (exp(2*(Z_T0S0+(qnorm(1-Alpha/2)*sqrt(1/(N.total-3)))))-1)/(exp(2*(Z_T0S0+(qnorm(1-Alpha/2)*sqrt(1/(N.total-3)))))+1))
rho_sd <- sqrt((1-T0S0**2)/(N.total-2))
rho_results_T0S0 <- data.frame(cbind(T0S0, rho_sd , rho_lb, rho_ub), stringsAsFactors = TRUE)
colnames(rho_results_T0S0) <- c("Estimate", "Standard Error", "CI lower limit", "CI upper limit")
rownames(rho_results_T0S0) <- c(" ")
Z_T1S1 <- .5*log((1+T1S1)/(1-T1S1))
rho_lb <- max(0, (exp(2*(Z_T1S1-(qnorm(1-Alpha/2)*sqrt(1/(N.total-3)))))-1)/(exp(2*(Z_T1S1-(qnorm(1-Alpha/2)*sqrt(1/(N.total-3)))))+1))
rho_ub <- min(1, (exp(2*(Z_T1S1+(qnorm(1-Alpha/2)*sqrt(1/(N.total-3)))))-1)/(exp(2*(Z_T1S1+(qnorm(1-Alpha/2)*sqrt(1/(N.total-3)))))+1))
rho_sd <- sqrt((1-T1S1**2)/(N.total-2))
rho_results_T1S1 <- data.frame(cbind(T1S1, rho_sd , rho_lb, rho_ub), stringsAsFactors = TRUE)
colnames(rho_results_T1S1) <- c("Estimate", "Standard Error", "CI lower limit", "CI upper limit")
rownames(rho_results_T1S1) <- c(" ")
Cor.Endpoints <- data.frame(rbind(rho_results_T0S0, rho_results_T1S1), stringsAsFactors = TRUE)
rownames(Cor.Endpoints) <- c("r_T0S0", "r_T1S1")
colnames(Cor.Endpoints) <- c("Estimate", "Standard Error", "CI lower limit", "CI upper limit")
T0T0 = var(T0); T1T1 = var(T1); S0S0 = var(S0); S1S1 = var(S1)
fit <-
list(Data.Analyze=wide, Obs.Per.Trial=Obs.per.trial, Trial.Spec.Results=Trial.Spec.Results, Residuals=Residuals,
Fixed.Effect.Pars=Fixed.Effect.Pars, Random.Effect.Pars=Random.Effect.Pars, Trial.R2=Trial.R2, Trial.R=Trial.R,
Indiv.R2=Indiv.R2, Indiv.R=Indiv.R, Cor.Endpoints=Cor.Endpoints, D=D, Sigma=VarCovarResid,
ICA=ICA, T0T0 = T0T0, T1T1 = T1T1, S0S0 = S0S0, S1S1 = S1S1, Call=match.call())
class(fit) <- "BimixedContCont"
fit
}
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