Nothing
beta/mlma.R
In ipdmeta: Tools for subgroup analyses with multiple trial data using aggregate statistics
mlma <- function(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction,
max.iter=5,
min.sample=300,
est.delta=.1,
mc.step=2,
df=30,
echo = TRUE,
max.sample = NULL,
converge.index,
error=1/100,
fixed=FALSE
){
### MISSING REMOVAL
vars <- all.vars(ipd.formula)
if(!fixed) vars <- c(vars,all.vars(random.formula))
missing.cases <- sapply(vars,function(x){any(is.na(ipd.data[,x]))})
if(any(missing.cases)){
i <- which(missing.cases)
missing.index <- sapply(vars[i],function(x){which(is.na(ipd.data[,x]))})
missing.index <- unique(as.vector(unlist(missing.index)))
ipd.data <- ipd.data[-missing.index,]
print(paste(length(missing.index),"Missing cases excluded"))
}
if(fixed){
fit <- coxmeta.fixed(
ipd.formula=ipd.formula,
meta.formula=meta.formula,
ipd.data=ipd.data,
meta.data=meta.data,
sigma2=sigma2,
study.group=study.group.interaction,
error=error)
}
else{
fit <- coxmeta.mixed(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction,
max.iter,
min.sample,
est.delta,
mc.step,
df,
echo,
max.sample,
converge.index,
error)
}
return(fit)
}
###DEPENDENCIES
coxmeta.fixed <- function(
ipd.formula,
meta.formula,
ipd.data,
meta.data,
sigma2,
study.group.interaction,
error=1/1000)
{
beta.index <- beta.indices(ipd.formula,meta.formula)
beta.ad = runif(length(beta.index$ad))
beta.ipd = runif(max(beta.index$ipd))
#KNOWN COVARIANCE-VARIANCE FOR SURVIVAL OUTCOMES
s <- meta.data[,all.vars(meta.formula)[[1]]]
Sigma <- surv.covariance.variance(s,sigma2,study.group.interaction)
####NEWTON-RAPHSON MAXIMIZATION
converge <- error*2
loglik <- 0
last <- FALSE
monitor <- list(coef=c(),loglik=c())
while(converge>error|!last){
if(converge<error) last = TRUE
#PATIENT LEVEL SCORE/INFO/LIKELIHOOD
NR.cox <- coxph.with.offset(ipd.formula,ipd.data,beta=beta.ipd)
#STUDY LEVEL SCORE/INFO/LIKELIHOOD
NR.surv <- surv.with.offset(meta.formula,meta.data,beta=beta.ad,Sigma)
#COMBINED
NR <- combined.score.info(
beta.index,
U.cox=NR.cox$score,
I.cox=NR.cox$info,
loglik.cox=NR.cox$loglik,
U.surv=NR.surv$score,
I.surv=NR.surv$info,
loglik.surv=NR.surv$loglik)
#UPDATE
update <- as.vector(solve(NR$info)%*%NR$score)
beta.ipd <- beta.ipd+update[beta.index$ipd]
beta.ad <- beta.ad+update[beta.index$ad]
beta <- beta.construct(beta.ipd,beta.ad,beta.index$shared)
converge = abs(NR$loglik-loglik)
loglik = NR$loglik
monitor$coef <- cbind(monitor$coef,beta)
monitor$loglik <- c(monitor$loglik,loglik)
}
beta <- array(beta)
names(beta) <- beta.index$labels
return(
list(
coef=beta,
var=solve(NR$info),
loglik=loglik,
monitor=monitor)
)
}
combined.score.info <- function(
beta.index,
U.cox,
I.cox,
loglik.cox,
U.surv,
I.surv,
loglik.surv)
{
p <- max(beta.index$ad)
U <- rep(0,p)
U[beta.index$ipd] <- U.cox
U[beta.index$ad] <- U[beta.index$ad]+U.surv
#COMBINING INFO
I <- matrix(0,p,p)
I[beta.index$ipd,beta.index$ipd] <- I.cox
I[beta.index$ad,beta.index$ad] <- I[beta.index$ad,beta.index$ad]+I.surv
loglik <- loglik.cox+loglik.surv
return(
list(
score=U,
info=I,
loglik=loglik)
)
}
coxph.with.offset <- function(
formula,
data,
beta,
offset)
{
#FORMULA CONSTRUCTION
names <- all.vars(terms(formula))
time <- data[,names[1]]
event <- data[,names[2]]
S <- Surv(time,event)
covariates <- paste(attr(terms(formula),"term.labels"),collapse="+")
f <- as.formula(paste(c("S~",covariates),c=""))
if(!missing(offset)) f <- as.formula(paste(c(f,"offset(offset)"),
collapse="+"))
#MODEL ESTIMATION AT BETA/OFFSET
fit <- coxph(f,data=data,init=beta,iter=0)
fit.detail <- coxph.detail(fit)
#SCORE/INFO/LOGLIK
p <- length(beta)
U <- fit.detail$score
if(is.matrix(U)) U <- colSums(U) else U <- sum(U)
I <- matrix(as.vector(fit.detail$imat),nrow=p^2)
I <- apply(I,1,sum)
I <- matrix(I,p,p)
return(
list(
score=U,
info=I,
loglik=fit$loglik[2])
)
}
surv.with.offset <- function(
formula,
data,
beta,
Sigma,
offset)
{
X <- model.matrix(terms(formula),data)
S <- data[,all.vars(terms(formula))[1]]
R <- log(-log(S))-X%*%beta
if(!missing(offset)) R <- R-offset
U <- as.vector(t(X)%*%solve(Sigma)%*%R)
I <- as.vector(t(X)%*%solve(Sigma)%*%X)
I <- matrix(I,length(beta),length(beta))
loglik <- -1/2*t(R)%*%solve(Sigma)%*%R
return(
list(
score=U,
info=I,
loglik=loglik)
)
}
coxmeta.mixed <- function(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction,
max.iter,
min.sample,
est.delta,
mc.step,
df,
echo = TRUE,
max.sample=NULL,
converge.index,
error)
{
###INITIALIZATION
beta.index <- beta.indices(ipd.formula,meta.formula)
coxmeta.objects <-
coxmeta.initialize(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction,
beta.index
)
D = coxmeta.objects$params$D
fixed.coxmeta <- coxmeta.fixed(
ipd.formula,
meta.formula,
ipd.data,
meta.data,
sigma2,
study.group.interaction,
error=error
)
D = coxmeta.objects$params$D
beta.ipd = fixed.coxmeta$coef[beta.index$ipd]
beta.ad = fixed.coxmeta$coef[beta.index$ad]
loglik.null = fixed.coxmeta$loglik
p.D = dim(D)[1]
if(missing(converge.index)) converge.index = 1:(max(beta.index$ad)+p.D)
###PROPOSAL SAMPLING
rmt.cox <- function(n){rmt(n,coxmeta.objects$params$cox.loc,
coxmeta.objects$params$cox.scale,df=df)}
rmt.surv <- function(n){rmt(n,coxmeta.objects$params$surv.loc,
coxmeta.objects$params$surv.scale,df=df)}
weights <- function(B.cox,B.surv){
coxmeta.importance.weights(
coxmeta.objects$model[[1]],coxmeta.objects$model[[2]],
B.cox,B.surv,D,beta.cox=beta.ipd,beta.surv=beta.ad,
coxmeta.objects$params$cox.loc,coxmeta.objects$params$cox.scale,
coxmeta.objects$params$surv.loc,coxmeta.objects$params$surv.scale,df)
}
#CONVERGENCE/SAMPLE SIZE INITIALIZATION
N = min.sample
if(!is.null(max.sample)) fixed.escalation = seq(min.sample,max.sample,length=max.iter)
n.iter <- 0
converged = FALSE
#COXMCEM OUTPUT
coxmcem.object <-
list(
max.weight=c(),
mc.samples=c(),
est.converge=c(),
sd.loglik=c(),
loglik=c()
)
while(!converged&n.iter<max.iter){
n.iter <- n.iter+1
#SAMPLING RANDOM EFFECTS
B.cox <- rmt.cox(N)
B.surv <- rmt.surv(N)
#IMPORTANCE WEIGHTS
weight.object <- weights(B.cox,B.surv)
w <- weight.object$weights
#ESTEP
Estep <-
coxmeta.Estep(coxmeta.objects$model[[1]],coxmeta.objects$model[[2]],
B.cox,B.surv,weights=w)
#MSTEP
Mstep <- coxmeta.Mstep(beta.index,beta.ipd,beta.ad,
coxmeta.objects$model[[1]],
coxmeta.objects$model[[2]],
Estep)
###CONVERGENCE CRITERIA
###RELATIVE CHANGE
beta = beta.construct(beta.ipd,beta.ad,beta.index$shared)
zeta.now = c(Mstep$fit$coef,diag(Mstep$D))
zeta.last = c(beta,get.diag(D))
est.converge <- relative.criteria(zeta.now[converge.index],
zeta.last[converge.index])
###UPDATE COX OBJECT
coxmcem.object$max.weight <- append(coxmcem.object$max.weight,max(w))
tries <- length(coxmcem.object$max.weight)
m <- length(coxmcem.object$est.converge)
if(tries>3) cv.last <- sd(coxmcem.object$est.converge[(m-2):m])/mean(coxmcem.object$est.converge[(m-2):m])
coxmcem.object$est.converge <- append(coxmcem.object$est.converge,
est.converge)
if(tries>3) cv.now <- sd(coxmcem.object$est.converge[(m-1):(m+1)])/mean(coxmcem.object$est.converge[(m-1):(m+1)])
coxmcem.object$mc.samples <- append(coxmcem.object$mc.samples,N)
coxmcem.object$sd.loglik <- append(coxmcem.object$sd.loglik,
weight.object$sd.loglik)
coxmcem.object$loglik <- append(coxmcem.object$loglik,
weight.object$loglik)
###CHECK IF CONVERGENCE IS MET; UPDATE SAMPLE SIZE IF NOT
if(tries>2){
est.convergence <- max(coxmcem.object$est.converge[(m-1):(m+1)])
if(est.convergence<est.delta){
converged = TRUE
}
}
if(is.null(max.sample)){
if(tries>3){
if(cv.now>cv.last) N = ceiling(N+N/mc.step)
}
}
else{
N = ceiling(fixed.escalation[n.iter])
}
#UPDATE ESTIMATES; PRINT CONVERGENCE
if(tries>3&echo) print(paste(c("N","Max Weight","% Relative Change"),
round(c(coxmcem.object$mc.samples[length(coxmcem.object$mc.samples)],
max(w),est.converge*100),5)))
###UPDATE
beta.ipd = Mstep$fit$coef[beta.index$ipd]
beta.ad = Mstep$fit$coef[beta.index$ad]
D = Mstep$D
}
#FINAL ESTIMATES
coxmcem.object$iterations <- n.iter
coxmcem.object$coef <- beta.construct(beta.ipd,beta.ad,beta.index$shared)
coxmcem.object$vcov <- D
coxmcem.object$cluster <- rbind(Estep$cox.mean,Estep$surv.mean)
#VARIANCE ESTIMATION
coxmcem.object$var <- list()
coxmcem.object$var$coef <-
coxmeta.info.coef(beta.index,beta.ipd,beta.ad,
cox=coxmeta.objects$model[[1]],
surv=coxmeta.objects$model[[2]],
B.cox,B.surv,w)
coxmcem.object$var$coef <- solve(coxmcem.object$var$coef)
coxmcem.object$var$vcov <-
coxmeta.info.vcov(B.cox,B.surv,ipd.groups,meta.groups,D,w)
coxmcem.object$var$vcov <- 1/coxmcem.object$var$vcov
return(coxmcem.object)
}
coxmeta.Estep <- function(
cox,
surv,
B.cox,
B.surv,
weights)
{
#Patient-level offsets
u <- apply(B.cox,1,function(b){
cox$Z%*%b
})
#RETURNS THE SAMPLED OFFSETS BY COLUMN
#OBTAIN EACH ROWS WEIGHTED AVERAGE
u <- u%*%weights
#STUDY-LEVEL OFFSETS
v <- apply(B.surv,1,function(b){
surv$Z%*%b
})
v <- v%*%weights
#WEIGHTED MEANS
cox.means <- t(B.cox)%*%weights
surv.means <- t(B.surv)%*%weights
#WEIGHTED SECOND MOMENT
#RETURNS A VECTOR OF THE STACKED SUBVECTORS
V.cox <- apply(B.cox,1,function(b){
b <- cluster.subvector.matrix(b,n.groups=cox$n.groups)
apply(b,1,function(x){outer(x,x)})
})
V.cox <- V.cox%*%weights
V.surv <- apply(B.surv,1,function(b){
b <- cluster.subvector.matrix(b,n.groups=surv$n.groups)
apply(b,1,function(x){outer(x,x)})
})
V.surv <- V.surv%*%weights
return(
list(
cox.offset=u,
surv.offset=v,
cox.mean=cox.means,
surv.mean=surv.means,
cox.second=V.cox,
surv.second=V.surv)
)
}
coxmeta.Mstep <- function(
beta.index,
beta.ipd,
beta.ad,
cox,
surv,
Estep)
{
#BETA PARAMETERS
fit <- coxmeta.offset(beta.index,beta.ipd,
beta.ad,cox,surv,
Estep$cox.offset,Estep$surv.offset)
#VARIANCE PARAMETERS
D <- cluster.variance(cox,surv,Estep$cox.second,Estep$surv.second)
return(list(fit=fit,D=D))
}
#MAXIMIZATION OF VARIANCE COMPONENTS
cluster.variance <- function(
cox,
surv,
cox.second,
surv.second)
{
ncox <- length(cox.second)
nsurv <- length(surv.second)
D.cox <- cluster.subvector.matrix(cox.second,ncox/cox$n.groups)
D.surv <- cluster.subvector.matrix(cox.second,nsurv/surv$n.groups)
D <- cbind(D.cox,D.surv)
D <- apply(D,1,mean)
Ddim <- sqrt(length(D))
matrix(D,Ddim,Ddim)
}
#MAXIMIZATION OF EFFECTS
coxmeta.offset <- function(
beta.index,
beta.ipd,
beta.ad,
cox,
surv,
cox.offset,
surv.offset,
error=1/1000)
{
#KNOWN COVARIANCE-VARIANCE FOR SURVIVAL OUTCOMES
Sigma <- surv$Sigma
####NEWTON-RAPHSON MAXIMIZATION
converge <- error*2
loglik <- 0
last <- FALSE
monitor <- list(coef=c(),loglik=c())
while(converge>error|!last){
if(converge<error) last = TRUE
#PATIENT LEVEL SCORE/INFO/LIKELIHOOD
NR.cox <- coxph.with.offset(cox$f,cox$data,beta=beta.ipd,cox.offset)
#STUDY LEVEL SCORE/INFO/LIKELIHOOD
NR.surv <- surv.with.offset(surv$f,surv$data,beta=beta.ad,Sigma,surv.offset)
#COMBINED
NR <- combined.score.info(
beta.index,
U.cox=NR.cox$score,
I.cox=NR.cox$info,
loglik.cox=NR.cox$loglik,
U.surv=NR.surv$score,
I.surv=NR.surv$info,
loglik.surv=NR.surv$loglik)
#UPDATE
update <- as.vector(solve(NR$info)%*%NR$score)
beta.ipd <- beta.ipd+update[beta.index$ipd]
beta.ad <- beta.ad+update[beta.index$ad]
beta <- beta.construct(beta.ipd,beta.ad,beta.index$shared)
converge = abs(NR$loglik-loglik)
loglik = NR$loglik
monitor$coef <- cbind(monitor$coef,beta)
monitor$loglik <- c(monitor$loglik,loglik)
}
return(
list(
coef=beta,
var=solve(NR$info),
loglik=loglik,
monitor=monitor)
)
}
coxmeta.initialize <- function(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction,
beta.index)
{
model.objects <- coxmeta.data.objects(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction
)
parameters <- parameter.initialize(
random.formula,
model.objects[[1]],
model.objects[[2]],
beta.index)
return(
list(
model=model.objects,
params=parameters)
)
}
parameter.initialize <- function(
random.formula,
cox.data.object,
surv.data.object,
beta.index)
{
f.random <- as.character(random.formula)[2]
phmm.fixed <- sub("(.*\\| ?)(.*)","cluster(\\2",f.random)
phmm.fixed <- paste(c(cox.data.object$f,phmm.fixed),collapse="+")
phmm.random <- sub("\\((.*)\\| ?(.*)","~\\1",f.random)
coxme.formula <- paste(
c(cox.data.object$f,as.character(random.formula)[2]),
collapse="+")
coxme.formula <- formula(coxme.formula)
phmm.formula <- list(fixed=formula(phmm.fixed),
random=formula(phmm.random))
if(cox.data.object$n.groups>1){
cox.params <- coxmcem.mixed.initialize(
coxme.formula,
phmm.formula,
cox.data.object$data
)
D = as.matrix(cox.params$D)
beta.ipd = cox.params$beta
cox.loc <- cox.params$mean
cox.scale <- cox.params$Sigma
}
else{
D = make.diagonal.matrix(runif(ncol(cox.data.object$Z)))
beta.ipd = rnorm(length(beta.index$ipd))
}
beta.ad = runif(length(beta.index$ad))
beta.ad[beta.index$shared.index] <- beta.ipd[beta.index$ad[beta.index$shared.index]]
beta = beta.construct(beta.ipd,beta.ad,beta.index$shared.index)
DA <- diag(rep(diag(D),each=surv.data.object$n.groups))
scale <- solve(solve(DA)+t(surv.data.object$Z)%*%solve(surv.data.object$Sigma)%*%surv.data.object$Z)
R <- log(-log(surv.data.object$S))-surv.data.object$X%*%beta.ad
loc <- scale%*%t(surv.data.object$Z)%*%solve(surv.data.object$Sigma)%*%R
loc <- as.numeric(loc)
n.frailties = length(loc)
N = surv.data.object$n.groups*(nrow(cox.data.object$data)/cox.data.object$n.groups)
scale = diag(rep(n.frailties/N,n.frailties))
if(cox.data.object$n.groups==1){
cox.loc <- cluster.subvector.matrix(loc,surv.data.object$n.groups)
cox.loc <- apply(cox.loc,2,mean)
n.scale <- length(cox.loc)
cox.scale <- make.diagonal.matrix(rep(scale[1,1],n.scale))
}
return(list(
beta.ipd = beta.ipd,
beta.ad = beta.ad,
D = D,
cox.loc = cox.loc,
cox.scale = cox.scale,
surv.loc = loc,
surv.scale = scale)
)
}
###CREATING DATA OBJECTS
coxmeta.data.objects <- function(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction)
{
#MAKING DATA OBJECTS
cox.data.object <- list(
f=ipd.formula,
Z=frailty.model.matrix(random.formula,ipd.data),
data=ipd.data,
n.groups=ipd.groups
)
#MAKE SURV DATA OBJECT
surv <- meta.data[,all.vars(meta.formula)[1]]
surv.data.object <- list(
f=meta.formula,
data=meta.data,
S=surv,
X=model.matrix(terms(meta.formula),meta.data),
Z=frailty.model.matrix(random.formula,meta.data),
Sigma=surv.covariance.variance(surv,sigma2,study.group.interaction),
n.groups=meta.groups
)
return(
list(
cox.data.object,
surv.data.object)
)
}
coxmeta.info.coef <- function(
beta.index,
beta.ipd,
beta.ad,
cox,surv,
B.cox,
B.surv,
weights)
{
cox.offset <- cox$Z%*%t(B.cox)%*%weights
surv.offset <- surv$Z%*%t(B.surv)%*%weights
info.beta <- coxmeta.score.info.coef(
beta.index,
beta.ipd,
beta.ad,
cox,surv,
cox.offset,surv.offset
)
info.beta <- as.vector(info.beta$info)
b.cox.offset <- lapply(1:nrow(B.cox),function(i){cox$Z%*%B.cox[i,]})
b.surv.offset <- lapply(1:nrow(B.surv),function(i){surv$Z%*%B.surv[i,]})
scores.beta <- mapply(
coxmeta.score.info.coef,
cox.offset=b.cox.offset,
surv.offset=b.surv.offset,
MoreArgs=list(
beta.index=beta.index,
beta.ipd=beta.ipd,
beta.ad=beta.ad,
cox=cox,surv=surv,score.only=TRUE))
U2.beta <- if(is.matrix(scores.beta)) apply(scores.beta,2,function(x){outer(x,x)}) else scores.beta^2
U2.beta <- U2.beta%*%weights
###LOUIS INFO FOR EFFECTS
info.beta <- info.beta-U2.beta
info.beta <- matrix(info.beta,
sqrt(length(info.beta)),sqrt(length(info.beta)))
return(info.beta)
}
coxmeta.score.info.coef <- function(
beta.index,
beta.ipd,
beta.ad,
cox,
surv,
cox.offset,
surv.offset,
score.only=FALSE)
{
#KNOWN COVARIANCE-VARIANCE FOR SURVIVAL OUTCOMES
Sigma <- surv$Sigma
#PATIENT LEVEL SCORE/INFO/LIKELIHOOD
NR.cox <- coxph.with.offset(cox$f,cox$data,beta=beta.ipd,cox.offset)
#STUDY LEVEL SCORE/INFO/LIKELIHOOD
NR.surv <- surv.with.offset(surv$f,surv$data,beta=beta.ad,Sigma,surv.offset)
#COMBINED
NR <- combined.score.info(
beta.index=beta.index,
U.cox=NR.cox$score,
I.cox=NR.cox$info,
loglik.cox=NR.cox$loglik,
U.surv=NR.surv$score,
I.surv=NR.surv$info,
loglik.surv=NR.surv$loglik)
if(!score.only) return(list(info=NR$info,score=NR$score)) else NR$score
}
#####VARIANCE PARAMETERS
coxmeta.info.vcov <- function(
B.cox,
B.surv,
ipd.groups,
meta.groups,
D,
weights)
{
info.var <- mixed.vcov.info(D,ipd.groups+meta.groups)
U2.var <- mixed.vcov.score(B.cox,B.surv,D,ipd.groups,meta.groups)
U2.var <- U2.var%*%weights
info.var <- ifelse(info.var-U2.var>0,info.var-U2.var,info.var)
return(info.var)
}
mixed.vcov.info <- function(
D,
n.groups)
{
f <- function(x){n.groups/(2*x^2)}
theta <- get.diag(D)
sapply(theta,f)
}
mixed.vcov.score <- function(
B.cox,
B.surv,
D,
cox.groups,
surv.groups)
{
###RETURNS THE QUADRATIC OF VCOV SCORES FOR DIAGONAL COMPONENTS
score <- function(
b.cox,
b.surv,
D,
cox.groups,
surv.groups)
{
n <- dim(D)[1]
b.mat <-
combined.subvector.matrix(b.cox,b.surv,cox.groups,surv.groups)
scr <- function(i){
-1/2*(length(b.mat[,i])/D[i,i]-sum(b.mat[,i]^2)/D[i,i]^2)
}
sapply(1:n,scr)^2
}
b.cox.list <- lapply(1:nrow(B.cox),function(i){B.cox[i,]})
b.surv.list <- lapply(1:nrow(B.surv),function(i){B.surv[i,]})
mapply(score,b.cox=b.cox.list,b.surv=b.surv.list,
MoreArgs=list(D=D,cox.groups=cox.groups,surv.groups=surv.groups))
}
coxmcem.mixed.initialize <- function(
coxme.formula,
phmm.formula,
data,
init.coef,
init.vcov)
{
#TRY COXME FOR INITIAL PARAMETERS AND FRAILTY PROPOSAL
#IF FAILURE GO TO PHMM
#COXME FORMULA
#PHMM FORMULA IS A LIST WITH FIXED AND RANDOM PARTS
no.error <- tryCatch(coxme(coxme.formula,data),
error=function(void){FALSE})
if(!is.logical(no.error)) no.error = TRUE
if(no.error){
fit <- coxme(coxme.formula,data)
coef <- fit$coef$fixed
vcov <- fit$coef$random[[1]]
mean <- as.vector(fit$frail[[1]])
}
else{
fit <- phmm(phmm.formula$fixed,phmm.formula$random,data)
coef <- fit$coef
vcov <- fit$Sigma
mean <- as.vector(fit$bhat)
}
if(!missing(init.coef)) coef <- init.coef
if(!missing(init.vcov)) vcov <- init.vcov
n.frailties <- length(mean) #SCALE BASED ON EFFECTIVE
n.eff <- nrow(data)/n.frailties #OBS PER FRAILTY
Sigma <- diag(rep(1/n.eff,n.frailties))
return(
list(
beta=coef,
D=vcov,
mean=mean,
Sigma=Sigma)
)
}
coxmeta.importance.weights <- function(
cox,
surv,
B.cox,
B.surv,
D,
beta.cox,
beta.surv,
loc.cox,
scale.cox,
loc.surv,
scale.surv,
df)
{
LL <- coxmeta.frailty.loglik(cox,surv,B.cox,B.surv,D,beta.cox,beta.surv)
LL.proposal.cox <- proposal.dmt(B.cox,loc.cox,scale.cox,df)
LL.proposal.surv <- proposal.dmt(B.surv,loc.surv,scale.surv,df)
LL.proposal <- LL.proposal.cox+LL.proposal.surv
w <- importance.weights(LL,LL.proposal)
return(list(weights=w,sd.loglik=sd(LL),loglik=LL%*%w))
}
######LIKELIHOOD COMPONENTS OF DATA FOR GIVEN FRAILTY
coxmeta.frailty.loglik <- function(
cox,
surv,
B.cox,
B.surv,
D,
beta.cox,
beta.surv)
{
loglik.cox <- apply(B.cox,1,function(b){
cox.mixed.loglik(cox$f,cox$data,cox$Z,b,beta.cox)
})
loglik.surv <- apply(B.surv,1,function(b){
surv.mixed.loglik(surv$S,surv$X,beta.surv,surv$Z,b,surv$Sigma)
})
loglik.penalty.cox <- penalty.mixed.loglik(t(B.cox),D,cox$n.groups)
loglik.penalty.surv <- penalty.mixed.loglik(t(B.surv),D,surv$n.groups)
loglik = loglik.cox+loglik.surv+loglik.penalty.cox+loglik.penalty.surv
return(loglik)
}
cox.mixed.loglik <- function(
f,
data,
Z,
b,
beta)
{
offset <- Z%*%b
f <- paste(c(f,"offset(offset)"),collapse="+")
f <- as.formula(f)
#LOGLIK DATA CONDITIONAL ON CLUSTER EFFECTS
L1 <- coxph(f,data,init=beta,iter=0)$loglik[2]
return(L1)
}
surv.mixed.loglik <- function(
S,
X,
beta,
Z,
b,
Sigma)
{
if(is.matrix(X)) u = X%*%beta else u=X*beta
R = log(-log(S))-u-Z%*%b
-1/2*t(R)%*%solve(Sigma)%*%R-1/2*log(det(Sigma))
}
penalty.mixed.loglik <- function(
B,
D,
n.groups)
{
#B HAS EACH COLUMN AS A SAMPLE OF FRAILTIES
#FRAILTIES ARE ORDERED BY COVARIATE, I.E. INTERCEPT, TRT, ETC.
sigma <- rep(get.diag(D),each=n.groups)
apply(-1/2*B^2/sigma,2,sum)-1/2*sum(log(sigma))
}
#######LIKELIHOOD FOR MULTIVARIATE T PROPOSAL
proposal.dmt <- function(
B,
loc,
scale,
df,
log=TRUE)
{
apply(B,1,function(b){
dmt(b,mean=loc,S=scale,df=df,log=log)
})
}
importance.weights <- function(
loglik,
loglik.proposal)
{
w <- loglik-loglik.proposal-max(loglik-loglik.proposal)
exp(w)/sum(exp(w))
}
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ipdmeta documentation built on May 2, 2019, 3:29 p.m.
R Package Documentation
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mlma <- function(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction,
max.iter=5,
min.sample=300,
est.delta=.1,
mc.step=2,
df=30,
echo = TRUE,
max.sample = NULL,
converge.index,
error=1/100,
fixed=FALSE
){
### MISSING REMOVAL
vars <- all.vars(ipd.formula)
if(!fixed) vars <- c(vars,all.vars(random.formula))
missing.cases <- sapply(vars,function(x){any(is.na(ipd.data[,x]))})
if(any(missing.cases)){
i <- which(missing.cases)
missing.index <- sapply(vars[i],function(x){which(is.na(ipd.data[,x]))})
missing.index <- unique(as.vector(unlist(missing.index)))
ipd.data <- ipd.data[-missing.index,]
print(paste(length(missing.index),"Missing cases excluded"))
}
if(fixed){
fit <- coxmeta.fixed(
ipd.formula=ipd.formula,
meta.formula=meta.formula,
ipd.data=ipd.data,
meta.data=meta.data,
sigma2=sigma2,
study.group=study.group.interaction,
error=error)
}
else{
fit <- coxmeta.mixed(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction,
max.iter,
min.sample,
est.delta,
mc.step,
df,
echo,
max.sample,
converge.index,
error)
}
return(fit)
}
###DEPENDENCIES
coxmeta.fixed <- function(
ipd.formula,
meta.formula,
ipd.data,
meta.data,
sigma2,
study.group.interaction,
error=1/1000)
{
beta.index <- beta.indices(ipd.formula,meta.formula)
beta.ad = runif(length(beta.index$ad))
beta.ipd = runif(max(beta.index$ipd))
#KNOWN COVARIANCE-VARIANCE FOR SURVIVAL OUTCOMES
s <- meta.data[,all.vars(meta.formula)[[1]]]
Sigma <- surv.covariance.variance(s,sigma2,study.group.interaction)
####NEWTON-RAPHSON MAXIMIZATION
converge <- error*2
loglik <- 0
last <- FALSE
monitor <- list(coef=c(),loglik=c())
while(converge>error|!last){
if(converge<error) last = TRUE
#PATIENT LEVEL SCORE/INFO/LIKELIHOOD
NR.cox <- coxph.with.offset(ipd.formula,ipd.data,beta=beta.ipd)
#STUDY LEVEL SCORE/INFO/LIKELIHOOD
NR.surv <- surv.with.offset(meta.formula,meta.data,beta=beta.ad,Sigma)
#COMBINED
NR <- combined.score.info(
beta.index,
U.cox=NR.cox$score,
I.cox=NR.cox$info,
loglik.cox=NR.cox$loglik,
U.surv=NR.surv$score,
I.surv=NR.surv$info,
loglik.surv=NR.surv$loglik)
#UPDATE
update <- as.vector(solve(NR$info)%*%NR$score)
beta.ipd <- beta.ipd+update[beta.index$ipd]
beta.ad <- beta.ad+update[beta.index$ad]
beta <- beta.construct(beta.ipd,beta.ad,beta.index$shared)
converge = abs(NR$loglik-loglik)
loglik = NR$loglik
monitor$coef <- cbind(monitor$coef,beta)
monitor$loglik <- c(monitor$loglik,loglik)
}
beta <- array(beta)
names(beta) <- beta.index$labels
return(
list(
coef=beta,
var=solve(NR$info),
loglik=loglik,
monitor=monitor)
)
}
combined.score.info <- function(
beta.index,
U.cox,
I.cox,
loglik.cox,
U.surv,
I.surv,
loglik.surv)
{
p <- max(beta.index$ad)
U <- rep(0,p)
U[beta.index$ipd] <- U.cox
U[beta.index$ad] <- U[beta.index$ad]+U.surv
#COMBINING INFO
I <- matrix(0,p,p)
I[beta.index$ipd,beta.index$ipd] <- I.cox
I[beta.index$ad,beta.index$ad] <- I[beta.index$ad,beta.index$ad]+I.surv
loglik <- loglik.cox+loglik.surv
return(
list(
score=U,
info=I,
loglik=loglik)
)
}
coxph.with.offset <- function(
formula,
data,
beta,
offset)
{
#FORMULA CONSTRUCTION
names <- all.vars(terms(formula))
time <- data[,names[1]]
event <- data[,names[2]]
S <- Surv(time,event)
covariates <- paste(attr(terms(formula),"term.labels"),collapse="+")
f <- as.formula(paste(c("S~",covariates),c=""))
if(!missing(offset)) f <- as.formula(paste(c(f,"offset(offset)"),
collapse="+"))
#MODEL ESTIMATION AT BETA/OFFSET
fit <- coxph(f,data=data,init=beta,iter=0)
fit.detail <- coxph.detail(fit)
#SCORE/INFO/LOGLIK
p <- length(beta)
U <- fit.detail$score
if(is.matrix(U)) U <- colSums(U) else U <- sum(U)
I <- matrix(as.vector(fit.detail$imat),nrow=p^2)
I <- apply(I,1,sum)
I <- matrix(I,p,p)
return(
list(
score=U,
info=I,
loglik=fit$loglik[2])
)
}
surv.with.offset <- function(
formula,
data,
beta,
Sigma,
offset)
{
X <- model.matrix(terms(formula),data)
S <- data[,all.vars(terms(formula))[1]]
R <- log(-log(S))-X%*%beta
if(!missing(offset)) R <- R-offset
U <- as.vector(t(X)%*%solve(Sigma)%*%R)
I <- as.vector(t(X)%*%solve(Sigma)%*%X)
I <- matrix(I,length(beta),length(beta))
loglik <- -1/2*t(R)%*%solve(Sigma)%*%R
return(
list(
score=U,
info=I,
loglik=loglik)
)
}
coxmeta.mixed <- function(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction,
max.iter,
min.sample,
est.delta,
mc.step,
df,
echo = TRUE,
max.sample=NULL,
converge.index,
error)
{
###INITIALIZATION
beta.index <- beta.indices(ipd.formula,meta.formula)
coxmeta.objects <-
coxmeta.initialize(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction,
beta.index
)
D = coxmeta.objects$params$D
fixed.coxmeta <- coxmeta.fixed(
ipd.formula,
meta.formula,
ipd.data,
meta.data,
sigma2,
study.group.interaction,
error=error
)
D = coxmeta.objects$params$D
beta.ipd = fixed.coxmeta$coef[beta.index$ipd]
beta.ad = fixed.coxmeta$coef[beta.index$ad]
loglik.null = fixed.coxmeta$loglik
p.D = dim(D)[1]
if(missing(converge.index)) converge.index = 1:(max(beta.index$ad)+p.D)
###PROPOSAL SAMPLING
rmt.cox <- function(n){rmt(n,coxmeta.objects$params$cox.loc,
coxmeta.objects$params$cox.scale,df=df)}
rmt.surv <- function(n){rmt(n,coxmeta.objects$params$surv.loc,
coxmeta.objects$params$surv.scale,df=df)}
weights <- function(B.cox,B.surv){
coxmeta.importance.weights(
coxmeta.objects$model[[1]],coxmeta.objects$model[[2]],
B.cox,B.surv,D,beta.cox=beta.ipd,beta.surv=beta.ad,
coxmeta.objects$params$cox.loc,coxmeta.objects$params$cox.scale,
coxmeta.objects$params$surv.loc,coxmeta.objects$params$surv.scale,df)
}
#CONVERGENCE/SAMPLE SIZE INITIALIZATION
N = min.sample
if(!is.null(max.sample)) fixed.escalation = seq(min.sample,max.sample,length=max.iter)
n.iter <- 0
converged = FALSE
#COXMCEM OUTPUT
coxmcem.object <-
list(
max.weight=c(),
mc.samples=c(),
est.converge=c(),
sd.loglik=c(),
loglik=c()
)
while(!converged&n.iter<max.iter){
n.iter <- n.iter+1
#SAMPLING RANDOM EFFECTS
B.cox <- rmt.cox(N)
B.surv <- rmt.surv(N)
#IMPORTANCE WEIGHTS
weight.object <- weights(B.cox,B.surv)
w <- weight.object$weights
#ESTEP
Estep <-
coxmeta.Estep(coxmeta.objects$model[[1]],coxmeta.objects$model[[2]],
B.cox,B.surv,weights=w)
#MSTEP
Mstep <- coxmeta.Mstep(beta.index,beta.ipd,beta.ad,
coxmeta.objects$model[[1]],
coxmeta.objects$model[[2]],
Estep)
###CONVERGENCE CRITERIA
###RELATIVE CHANGE
beta = beta.construct(beta.ipd,beta.ad,beta.index$shared)
zeta.now = c(Mstep$fit$coef,diag(Mstep$D))
zeta.last = c(beta,get.diag(D))
est.converge <- relative.criteria(zeta.now[converge.index],
zeta.last[converge.index])
###UPDATE COX OBJECT
coxmcem.object$max.weight <- append(coxmcem.object$max.weight,max(w))
tries <- length(coxmcem.object$max.weight)
m <- length(coxmcem.object$est.converge)
if(tries>3) cv.last <- sd(coxmcem.object$est.converge[(m-2):m])/mean(coxmcem.object$est.converge[(m-2):m])
coxmcem.object$est.converge <- append(coxmcem.object$est.converge,
est.converge)
if(tries>3) cv.now <- sd(coxmcem.object$est.converge[(m-1):(m+1)])/mean(coxmcem.object$est.converge[(m-1):(m+1)])
coxmcem.object$mc.samples <- append(coxmcem.object$mc.samples,N)
coxmcem.object$sd.loglik <- append(coxmcem.object$sd.loglik,
weight.object$sd.loglik)
coxmcem.object$loglik <- append(coxmcem.object$loglik,
weight.object$loglik)
###CHECK IF CONVERGENCE IS MET; UPDATE SAMPLE SIZE IF NOT
if(tries>2){
est.convergence <- max(coxmcem.object$est.converge[(m-1):(m+1)])
if(est.convergence<est.delta){
converged = TRUE
}
}
if(is.null(max.sample)){
if(tries>3){
if(cv.now>cv.last) N = ceiling(N+N/mc.step)
}
}
else{
N = ceiling(fixed.escalation[n.iter])
}
#UPDATE ESTIMATES; PRINT CONVERGENCE
if(tries>3&echo) print(paste(c("N","Max Weight","% Relative Change"),
round(c(coxmcem.object$mc.samples[length(coxmcem.object$mc.samples)],
max(w),est.converge*100),5)))
###UPDATE
beta.ipd = Mstep$fit$coef[beta.index$ipd]
beta.ad = Mstep$fit$coef[beta.index$ad]
D = Mstep$D
}
#FINAL ESTIMATES
coxmcem.object$iterations <- n.iter
coxmcem.object$coef <- beta.construct(beta.ipd,beta.ad,beta.index$shared)
coxmcem.object$vcov <- D
coxmcem.object$cluster <- rbind(Estep$cox.mean,Estep$surv.mean)
#VARIANCE ESTIMATION
coxmcem.object$var <- list()
coxmcem.object$var$coef <-
coxmeta.info.coef(beta.index,beta.ipd,beta.ad,
cox=coxmeta.objects$model[[1]],
surv=coxmeta.objects$model[[2]],
B.cox,B.surv,w)
coxmcem.object$var$coef <- solve(coxmcem.object$var$coef)
coxmcem.object$var$vcov <-
coxmeta.info.vcov(B.cox,B.surv,ipd.groups,meta.groups,D,w)
coxmcem.object$var$vcov <- 1/coxmcem.object$var$vcov
return(coxmcem.object)
}
coxmeta.Estep <- function(
cox,
surv,
B.cox,
B.surv,
weights)
{
#Patient-level offsets
u <- apply(B.cox,1,function(b){
cox$Z%*%b
})
#RETURNS THE SAMPLED OFFSETS BY COLUMN
#OBTAIN EACH ROWS WEIGHTED AVERAGE
u <- u%*%weights
#STUDY-LEVEL OFFSETS
v <- apply(B.surv,1,function(b){
surv$Z%*%b
})
v <- v%*%weights
#WEIGHTED MEANS
cox.means <- t(B.cox)%*%weights
surv.means <- t(B.surv)%*%weights
#WEIGHTED SECOND MOMENT
#RETURNS A VECTOR OF THE STACKED SUBVECTORS
V.cox <- apply(B.cox,1,function(b){
b <- cluster.subvector.matrix(b,n.groups=cox$n.groups)
apply(b,1,function(x){outer(x,x)})
})
V.cox <- V.cox%*%weights
V.surv <- apply(B.surv,1,function(b){
b <- cluster.subvector.matrix(b,n.groups=surv$n.groups)
apply(b,1,function(x){outer(x,x)})
})
V.surv <- V.surv%*%weights
return(
list(
cox.offset=u,
surv.offset=v,
cox.mean=cox.means,
surv.mean=surv.means,
cox.second=V.cox,
surv.second=V.surv)
)
}
coxmeta.Mstep <- function(
beta.index,
beta.ipd,
beta.ad,
cox,
surv,
Estep)
{
#BETA PARAMETERS
fit <- coxmeta.offset(beta.index,beta.ipd,
beta.ad,cox,surv,
Estep$cox.offset,Estep$surv.offset)
#VARIANCE PARAMETERS
D <- cluster.variance(cox,surv,Estep$cox.second,Estep$surv.second)
return(list(fit=fit,D=D))
}
#MAXIMIZATION OF VARIANCE COMPONENTS
cluster.variance <- function(
cox,
surv,
cox.second,
surv.second)
{
ncox <- length(cox.second)
nsurv <- length(surv.second)
D.cox <- cluster.subvector.matrix(cox.second,ncox/cox$n.groups)
D.surv <- cluster.subvector.matrix(cox.second,nsurv/surv$n.groups)
D <- cbind(D.cox,D.surv)
D <- apply(D,1,mean)
Ddim <- sqrt(length(D))
matrix(D,Ddim,Ddim)
}
#MAXIMIZATION OF EFFECTS
coxmeta.offset <- function(
beta.index,
beta.ipd,
beta.ad,
cox,
surv,
cox.offset,
surv.offset,
error=1/1000)
{
#KNOWN COVARIANCE-VARIANCE FOR SURVIVAL OUTCOMES
Sigma <- surv$Sigma
####NEWTON-RAPHSON MAXIMIZATION
converge <- error*2
loglik <- 0
last <- FALSE
monitor <- list(coef=c(),loglik=c())
while(converge>error|!last){
if(converge<error) last = TRUE
#PATIENT LEVEL SCORE/INFO/LIKELIHOOD
NR.cox <- coxph.with.offset(cox$f,cox$data,beta=beta.ipd,cox.offset)
#STUDY LEVEL SCORE/INFO/LIKELIHOOD
NR.surv <- surv.with.offset(surv$f,surv$data,beta=beta.ad,Sigma,surv.offset)
#COMBINED
NR <- combined.score.info(
beta.index,
U.cox=NR.cox$score,
I.cox=NR.cox$info,
loglik.cox=NR.cox$loglik,
U.surv=NR.surv$score,
I.surv=NR.surv$info,
loglik.surv=NR.surv$loglik)
#UPDATE
update <- as.vector(solve(NR$info)%*%NR$score)
beta.ipd <- beta.ipd+update[beta.index$ipd]
beta.ad <- beta.ad+update[beta.index$ad]
beta <- beta.construct(beta.ipd,beta.ad,beta.index$shared)
converge = abs(NR$loglik-loglik)
loglik = NR$loglik
monitor$coef <- cbind(monitor$coef,beta)
monitor$loglik <- c(monitor$loglik,loglik)
}
return(
list(
coef=beta,
var=solve(NR$info),
loglik=loglik,
monitor=monitor)
)
}
coxmeta.initialize <- function(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction,
beta.index)
{
model.objects <- coxmeta.data.objects(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction
)
parameters <- parameter.initialize(
random.formula,
model.objects[[1]],
model.objects[[2]],
beta.index)
return(
list(
model=model.objects,
params=parameters)
)
}
parameter.initialize <- function(
random.formula,
cox.data.object,
surv.data.object,
beta.index)
{
f.random <- as.character(random.formula)[2]
phmm.fixed <- sub("(.*\\| ?)(.*)","cluster(\\2",f.random)
phmm.fixed <- paste(c(cox.data.object$f,phmm.fixed),collapse="+")
phmm.random <- sub("\\((.*)\\| ?(.*)","~\\1",f.random)
coxme.formula <- paste(
c(cox.data.object$f,as.character(random.formula)[2]),
collapse="+")
coxme.formula <- formula(coxme.formula)
phmm.formula <- list(fixed=formula(phmm.fixed),
random=formula(phmm.random))
if(cox.data.object$n.groups>1){
cox.params <- coxmcem.mixed.initialize(
coxme.formula,
phmm.formula,
cox.data.object$data
)
D = as.matrix(cox.params$D)
beta.ipd = cox.params$beta
cox.loc <- cox.params$mean
cox.scale <- cox.params$Sigma
}
else{
D = make.diagonal.matrix(runif(ncol(cox.data.object$Z)))
beta.ipd = rnorm(length(beta.index$ipd))
}
beta.ad = runif(length(beta.index$ad))
beta.ad[beta.index$shared.index] <- beta.ipd[beta.index$ad[beta.index$shared.index]]
beta = beta.construct(beta.ipd,beta.ad,beta.index$shared.index)
DA <- diag(rep(diag(D),each=surv.data.object$n.groups))
scale <- solve(solve(DA)+t(surv.data.object$Z)%*%solve(surv.data.object$Sigma)%*%surv.data.object$Z)
R <- log(-log(surv.data.object$S))-surv.data.object$X%*%beta.ad
loc <- scale%*%t(surv.data.object$Z)%*%solve(surv.data.object$Sigma)%*%R
loc <- as.numeric(loc)
n.frailties = length(loc)
N = surv.data.object$n.groups*(nrow(cox.data.object$data)/cox.data.object$n.groups)
scale = diag(rep(n.frailties/N,n.frailties))
if(cox.data.object$n.groups==1){
cox.loc <- cluster.subvector.matrix(loc,surv.data.object$n.groups)
cox.loc <- apply(cox.loc,2,mean)
n.scale <- length(cox.loc)
cox.scale <- make.diagonal.matrix(rep(scale[1,1],n.scale))
}
return(list(
beta.ipd = beta.ipd,
beta.ad = beta.ad,
D = D,
cox.loc = cox.loc,
cox.scale = cox.scale,
surv.loc = loc,
surv.scale = scale)
)
}
###CREATING DATA OBJECTS
coxmeta.data.objects <- function(
ipd.formula,
meta.formula,
random.formula,
ipd.data,
meta.data,
ipd.groups,
meta.groups,
sigma2,
study.group.interaction)
{
#MAKING DATA OBJECTS
cox.data.object <- list(
f=ipd.formula,
Z=frailty.model.matrix(random.formula,ipd.data),
data=ipd.data,
n.groups=ipd.groups
)
#MAKE SURV DATA OBJECT
surv <- meta.data[,all.vars(meta.formula)[1]]
surv.data.object <- list(
f=meta.formula,
data=meta.data,
S=surv,
X=model.matrix(terms(meta.formula),meta.data),
Z=frailty.model.matrix(random.formula,meta.data),
Sigma=surv.covariance.variance(surv,sigma2,study.group.interaction),
n.groups=meta.groups
)
return(
list(
cox.data.object,
surv.data.object)
)
}
coxmeta.info.coef <- function(
beta.index,
beta.ipd,
beta.ad,
cox,surv,
B.cox,
B.surv,
weights)
{
cox.offset <- cox$Z%*%t(B.cox)%*%weights
surv.offset <- surv$Z%*%t(B.surv)%*%weights
info.beta <- coxmeta.score.info.coef(
beta.index,
beta.ipd,
beta.ad,
cox,surv,
cox.offset,surv.offset
)
info.beta <- as.vector(info.beta$info)
b.cox.offset <- lapply(1:nrow(B.cox),function(i){cox$Z%*%B.cox[i,]})
b.surv.offset <- lapply(1:nrow(B.surv),function(i){surv$Z%*%B.surv[i,]})
scores.beta <- mapply(
coxmeta.score.info.coef,
cox.offset=b.cox.offset,
surv.offset=b.surv.offset,
MoreArgs=list(
beta.index=beta.index,
beta.ipd=beta.ipd,
beta.ad=beta.ad,
cox=cox,surv=surv,score.only=TRUE))
U2.beta <- if(is.matrix(scores.beta)) apply(scores.beta,2,function(x){outer(x,x)}) else scores.beta^2
U2.beta <- U2.beta%*%weights
###LOUIS INFO FOR EFFECTS
info.beta <- info.beta-U2.beta
info.beta <- matrix(info.beta,
sqrt(length(info.beta)),sqrt(length(info.beta)))
return(info.beta)
}
coxmeta.score.info.coef <- function(
beta.index,
beta.ipd,
beta.ad,
cox,
surv,
cox.offset,
surv.offset,
score.only=FALSE)
{
#KNOWN COVARIANCE-VARIANCE FOR SURVIVAL OUTCOMES
Sigma <- surv$Sigma
#PATIENT LEVEL SCORE/INFO/LIKELIHOOD
NR.cox <- coxph.with.offset(cox$f,cox$data,beta=beta.ipd,cox.offset)
#STUDY LEVEL SCORE/INFO/LIKELIHOOD
NR.surv <- surv.with.offset(surv$f,surv$data,beta=beta.ad,Sigma,surv.offset)
#COMBINED
NR <- combined.score.info(
beta.index=beta.index,
U.cox=NR.cox$score,
I.cox=NR.cox$info,
loglik.cox=NR.cox$loglik,
U.surv=NR.surv$score,
I.surv=NR.surv$info,
loglik.surv=NR.surv$loglik)
if(!score.only) return(list(info=NR$info,score=NR$score)) else NR$score
}
#####VARIANCE PARAMETERS
coxmeta.info.vcov <- function(
B.cox,
B.surv,
ipd.groups,
meta.groups,
D,
weights)
{
info.var <- mixed.vcov.info(D,ipd.groups+meta.groups)
U2.var <- mixed.vcov.score(B.cox,B.surv,D,ipd.groups,meta.groups)
U2.var <- U2.var%*%weights
info.var <- ifelse(info.var-U2.var>0,info.var-U2.var,info.var)
return(info.var)
}
mixed.vcov.info <- function(
D,
n.groups)
{
f <- function(x){n.groups/(2*x^2)}
theta <- get.diag(D)
sapply(theta,f)
}
mixed.vcov.score <- function(
B.cox,
B.surv,
D,
cox.groups,
surv.groups)
{
###RETURNS THE QUADRATIC OF VCOV SCORES FOR DIAGONAL COMPONENTS
score <- function(
b.cox,
b.surv,
D,
cox.groups,
surv.groups)
{
n <- dim(D)[1]
b.mat <-
combined.subvector.matrix(b.cox,b.surv,cox.groups,surv.groups)
scr <- function(i){
-1/2*(length(b.mat[,i])/D[i,i]-sum(b.mat[,i]^2)/D[i,i]^2)
}
sapply(1:n,scr)^2
}
b.cox.list <- lapply(1:nrow(B.cox),function(i){B.cox[i,]})
b.surv.list <- lapply(1:nrow(B.surv),function(i){B.surv[i,]})
mapply(score,b.cox=b.cox.list,b.surv=b.surv.list,
MoreArgs=list(D=D,cox.groups=cox.groups,surv.groups=surv.groups))
}
coxmcem.mixed.initialize <- function(
coxme.formula,
phmm.formula,
data,
init.coef,
init.vcov)
{
#TRY COXME FOR INITIAL PARAMETERS AND FRAILTY PROPOSAL
#IF FAILURE GO TO PHMM
#COXME FORMULA
#PHMM FORMULA IS A LIST WITH FIXED AND RANDOM PARTS
no.error <- tryCatch(coxme(coxme.formula,data),
error=function(void){FALSE})
if(!is.logical(no.error)) no.error = TRUE
if(no.error){
fit <- coxme(coxme.formula,data)
coef <- fit$coef$fixed
vcov <- fit$coef$random[[1]]
mean <- as.vector(fit$frail[[1]])
}
else{
fit <- phmm(phmm.formula$fixed,phmm.formula$random,data)
coef <- fit$coef
vcov <- fit$Sigma
mean <- as.vector(fit$bhat)
}
if(!missing(init.coef)) coef <- init.coef
if(!missing(init.vcov)) vcov <- init.vcov
n.frailties <- length(mean) #SCALE BASED ON EFFECTIVE
n.eff <- nrow(data)/n.frailties #OBS PER FRAILTY
Sigma <- diag(rep(1/n.eff,n.frailties))
return(
list(
beta=coef,
D=vcov,
mean=mean,
Sigma=Sigma)
)
}
coxmeta.importance.weights <- function(
cox,
surv,
B.cox,
B.surv,
D,
beta.cox,
beta.surv,
loc.cox,
scale.cox,
loc.surv,
scale.surv,
df)
{
LL <- coxmeta.frailty.loglik(cox,surv,B.cox,B.surv,D,beta.cox,beta.surv)
LL.proposal.cox <- proposal.dmt(B.cox,loc.cox,scale.cox,df)
LL.proposal.surv <- proposal.dmt(B.surv,loc.surv,scale.surv,df)
LL.proposal <- LL.proposal.cox+LL.proposal.surv
w <- importance.weights(LL,LL.proposal)
return(list(weights=w,sd.loglik=sd(LL),loglik=LL%*%w))
}
######LIKELIHOOD COMPONENTS OF DATA FOR GIVEN FRAILTY
coxmeta.frailty.loglik <- function(
cox,
surv,
B.cox,
B.surv,
D,
beta.cox,
beta.surv)
{
loglik.cox <- apply(B.cox,1,function(b){
cox.mixed.loglik(cox$f,cox$data,cox$Z,b,beta.cox)
})
loglik.surv <- apply(B.surv,1,function(b){
surv.mixed.loglik(surv$S,surv$X,beta.surv,surv$Z,b,surv$Sigma)
})
loglik.penalty.cox <- penalty.mixed.loglik(t(B.cox),D,cox$n.groups)
loglik.penalty.surv <- penalty.mixed.loglik(t(B.surv),D,surv$n.groups)
loglik = loglik.cox+loglik.surv+loglik.penalty.cox+loglik.penalty.surv
return(loglik)
}
cox.mixed.loglik <- function(
f,
data,
Z,
b,
beta)
{
offset <- Z%*%b
f <- paste(c(f,"offset(offset)"),collapse="+")
f <- as.formula(f)
#LOGLIK DATA CONDITIONAL ON CLUSTER EFFECTS
L1 <- coxph(f,data,init=beta,iter=0)$loglik[2]
return(L1)
}
surv.mixed.loglik <- function(
S,
X,
beta,
Z,
b,
Sigma)
{
if(is.matrix(X)) u = X%*%beta else u=X*beta
R = log(-log(S))-u-Z%*%b
-1/2*t(R)%*%solve(Sigma)%*%R-1/2*log(det(Sigma))
}
penalty.mixed.loglik <- function(
B,
D,
n.groups)
{
#B HAS EACH COLUMN AS A SAMPLE OF FRAILTIES
#FRAILTIES ARE ORDERED BY COVARIATE, I.E. INTERCEPT, TRT, ETC.
sigma <- rep(get.diag(D),each=n.groups)
apply(-1/2*B^2/sigma,2,sum)-1/2*sum(log(sigma))
}
#######LIKELIHOOD FOR MULTIVARIATE T PROPOSAL
proposal.dmt <- function(
B,
loc,
scale,
df,
log=TRUE)
{
apply(B,1,function(b){
dmt(b,mean=loc,S=scale,df=df,log=log)
})
}
importance.weights <- function(
loglik,
loglik.proposal)
{
w <- loglik-loglik.proposal-max(loglik-loglik.proposal)
exp(w)/sum(exp(w))
}
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