Nothing
R/msfit.R
In mstate: Data Preparation, Estimation and Prediction in Multi-State Models
Defines functions
`msfit`
#' Compute subject-specific transition hazards with (co-)variances
#'
#' This function computes subject-specific or overall cumulative transition
#' hazards for each of the possible transitions in the multi-state model. If
#' requested, also the variances and covariances of the estimated cumulative
#' transition hazards are calculated.
#'
#' The data frame needs to have one row for each transition in the multi-state
#' model. An additional column \code{strata} (numeric) is needed to describe
#' for each transition to which stratum it belongs. The name has to be
#' \code{strata}, even if in the original \code{coxph} call another variable
#' was used. For details refer to de Wreede, Fiocco & Putter (2010). So far,
#' the results have been checked only for the \code{"breslow"} method of
#' dealing with ties in \code{\link[survival:coxph]{coxph}}, so this is
#' recommended.
#'
#' @param object A \code{\link[survival:coxph]{coxph}} object describing the
#' fit of the multi-state model
#' @param newdata A data frame with the same variable names as those that
#' appear in the \code{coxph} formula. Its use is somewhat different from
#' \code{\link[survival:survfit]{survfit}}. See Details. The argument
#' \code{newdata} may be omitted only if the right hand side of the formula in
#' the \code{coxph} object is \code{~strata(trans)}
#' @param variance A logical value indicating whether the (co-)variances of the
#' subject-specific transition hazards should be computed. Default is
#' \code{TRUE}
#' @param vartype A character string specifying the type of variances to be
#' computed (so only needed if \code{variance}=\code{TRUE}). Possible values
#' are \code{"aalen"} or \code{"greenwood"}
#' @param trans Transition matrix describing the states and transitions in the
#' multi-state model. See \code{trans} in \code{\link{msprep}} for more
#' detailed information
#' @return An object of class \code{"msfit"}, which is a list containing
#' \item{Haz }{A data frame with \code{time}, \code{Haz}, \code{trans},
#' containing the estimated subject-specific hazards for each of the
#' transitions in the multi-state model} \item{varHaz }{A data frame with
#' \code{time}, \code{Haz}, \code{trans1}, \code{trans2} containing the
#' variances (\code{trans1}=\code{trans2}) and covariances
#' (\code{trans1}<\code{trans2}) of the estimated hazards. This element is only
#' returned when \code{variance}=\code{TRUE}} \item{trans}{The transition
#' matrix used}
#' @author Hein Putter \email{H.Putter@@lumc.nl}
#' @seealso \code{\link{plot.msfit}}
#' @references Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics:
#' Competing risks and multi-state models. \emph{Statistics in Medicine}
#' \bold{26}, 2389--2430.
#'
#' Therneau TM, Grambsch PM (2000). \emph{Modeling Survival Data: Extending the
#' Cox Model}. Springer, New York.
#'
#' de Wreede LC, Fiocco M, and Putter H (2010). The mstate package for
#' estimation and prediction in non- and semi-parametric multi-state and
#' competing risks models. \emph{Computer Methods and Programs in Biomedicine}
#' \bold{99}, 261--274.
#'
#' de Wreede LC, Fiocco M, and Putter H (2011). mstate: An R Package for the
#' Analysis of Competing Risks and Multi-State Models. \emph{Journal of
#' Statistical Software}, Volume 38, Issue 7.
#' @keywords survival
#' @examples
#'
#' # transition matrix for illness-death model
#' tmat <- trans.illdeath()
#' # data in wide format, for transition 1 this is dataset E1 of
#' # Therneau & Grambsch (2000)
#' tg <- data.frame(illt=c(1,1,6,6,8,9),ills=c(1,0,1,1,0,1),
#' dt=c(5,1,9,7,8,12),ds=c(1,1,1,1,1,1),
#' x1=c(1,1,1,0,0,0),x2=c(6:1))
#' # data in long format using msprep
#' tglong <- msprep(time=c(NA,"illt","dt"),status=c(NA,"ills","ds"),
#' data=tg,keep=c("x1","x2"),trans=tmat)
#' # events
#' events(tglong)
#' table(tglong$status,tglong$to,tglong$from)
#' # expanded covariates
#' tglong <- expand.covs(tglong,c("x1","x2"))
#' # Cox model with different covariate
#' cx <- coxph(Surv(Tstart,Tstop,status)~x1.1+x2.2+strata(trans),
#' data=tglong,method="breslow")
#' summary(cx)
#' # new data, to check whether results are the same for transition 1 as
#' # those in appendix E.1 of Therneau & Grambsch (2000)
#' newdata <- data.frame(trans=1:3,x1.1=c(0,0,0),x2.2=c(0,1,0),strata=1:3)
#' msfit(cx,newdata,trans=tmat)
#'
#' @export
`msfit` <-
function(object, newdata, variance=TRUE, vartype=c("aalen","greenwood"), trans)
{
### Modeled after survfit.coxph from the survival library by Therneau;
### indeed much of the code is just copied. Survfit is also called
### for vartype="greenwood".
### Stripped some of the features (cluster, individual) and limited the
### choice of other (type, vartype).
### Input:
### object: a coxph object
### newdata: a dataset with K lines for K transitions, as in survfit;
### newdata should contain a column "strata", specifying to which
### strata in the coxph object the K lines of newdata correspond.
### Argument newdata may only be omitted when the formula ends
### with ~strata(trans)
### variance: if TRUE (default), estimated (co)variance of cumulative
### hazards is returned
### vartype: aalen (default), greenwood is only supported in absence
### of newdata
### trans: the transition matrix of the multi-state model
if(!is.null((object$call)$weights) || !is.null(object$weights))
stop("msfit cannot (yet) compute the result for a weighted model")
Terms <- terms(object)
strat <- attr(Terms, "specials")$strata
cluster <- attr(Terms, "specials")$cluster
if (length(cluster)) stop("cluster terms are not supported")
if (!is.null(attr(object$terms, "specials")$tt))
stop("msfit cannot yet process coxph models with a tt term")
resp <- attr(Terms, "variables")[attr(Terms, "response")]
nvar <- length(object$coefficients)
score <- exp(object$linear.predictors) # exp(beta^T Z_i)
vartype <- match.arg(vartype)
if (is.na(vartype)) stop("Invalid variance type specified")
# Recreate a copy of the data (formerly using coxph.getdata, now with model.frame)
# Two purposes. One to find largest time point to be used in end result.
# Second later for call to C.
has.strata <- !is.null(attr(object$terms, 'specials')$strata)
if (is.null(object$y) || is.null(object[['x']]) ||
!is.null(object$call$weights) ||
(has.strata && is.null(object$strata)) ||
!is.null(attr(object$terms, 'offset'))) {
mf <- model.frame(object)
}
else mf <- NULL #useful for if statements later
if (is.null(mf)) y <- object[['y']]
else {
y <- model.response(mf)
y2 <- object[['y']]
if (!is.null(y2) && any(dim(y2) != dim(y)))
stop("Could not reconstruct the y vector")
}
if (is.null(object[['x']])) x <- model.matrix(object, data=mf)
else x <- object[['x']]
n <- nrow(y)
if (n != object$n[1] || nrow(x) !=n)
stop("Failed to reconstruct the original data set")
# Here we find largest time point
type <- attr(y, 'type')
if (type=='counting') lasty <- max(y[,2]) # start, stop, status
else if (type=='right') lasty <- max(y[,1]) # time, status
else stop("Cannot handle \"", type, "\" type survival data")
if (is.null(mf)) offset <- 0
else {
offset <- model.offset(mf)
if (is.null(offset)) offset <- 0
}
Terms <- object$terms
if (!has.strata) strata <- rep(0L,n)
else {
stangle <- untangle.specials(Terms, 'strata') # used multiple times
strata <- object$strata #try this first
if (is.null(strata)){
if (length(stangle$vars)==1) strata <- mf[[stangle$vars]]
else strata <- strata(mf[, stangle$vars], shortlabel=TRUE)
}
}
if (has.strata) {
temp <- attr(Terms, "specials")$strata
factors <- attr(Terms, "factors")[temp,]
strata.interaction <- any(t(factors)*attr(Terms, "order") >1)
if (strata.interaction) stop("Interaction terms with strata not supported")
}
if (vartype=="greenwood") {
if (missing(trans))
stop("argument trans missing; needed for vartype=\"greenwood\"")
labels <- attr(Terms, "term.labels")
if (length(labels)!=1) stop("Invalid formula for greenwood, ~strata(trans) needed, no covariates allowed")
if (attr(Terms, "term.labels") != "strata(trans)") # This will happen if length=1 and different from strata(trans)
stop("Invalid formula for greenwood, ~strata(trans) needed, no covariates allowed")
sf0 <- summary(survfit(object))
norisk <- sf0$n.risk
noevent <- sf0$n.event
sf0 <- data.frame(time=sf0$time,Haz=-log(sf0$surv),norisk=norisk,
noevent=noevent, trans=as.numeric(sf0$strata))
allt <- sort(unique(c(sf0$time,lasty)))
nt <- length(allt)
K <- nrow(to.trans2(trans))
### Set up empty structures for Haz and varHaz
Haz <- data.frame(time=rep(allt,K),Haz=NA,trans=rep(1:K,rep(nt,K)))
if (variance) {
tr12 <- data.frame(trans1=rep(1:K,rep(K,K)),trans2=rep(1:K,K))
tr12 <- tr12[tr12$trans1<=tr12$trans2,]
varHaz <- data.frame(time=rep(allt,K*(K+1)/2),varHaz=0,
trans1=rep(tr12$trans1,rep(nt,K*(K+1)/2)),
trans2=rep(tr12$trans2,rep(nt,K*(K+1)/2)))
}
### Easiest to loop over starting states
S <- nrow(trans)
for (s in 1:S) {
trs <- trans[s,]
trs <- trs[!is.na(trs)]
ntrs <- length(trs)
if (ntrs > 0) {
for (i in 1:ntrs) {
trans1 <- trs[i]
sf1 <- sf0[sf0$trans==trans1,]
Haz$Haz[(trans1-1)*nt + match(sf1$time,allt)] <- sf1$Haz
Haz$Haz[(trans1-1)*nt + 1:nt] <- NAfix(Haz$Haz[(trans1-1)*nt + 1:nt],subst=0)
if (variance) {
varHaz1 <- cumsum((sf1$norisk-sf1$noevent)*sf1$noevent/sf1$norisk^3)
varHaz11 <- varHaz[varHaz$trans1==trans1 & varHaz$trans2==trans1,]
varHaz11$varHaz <- NA
varHaz11$varHaz[match(sf1$time,allt)] <- varHaz1
varHaz11$varHaz <- NAfix(varHaz11$varHaz,subst=0)
varHaz[varHaz$trans1==trans1 & varHaz$trans2==trans1,] <- varHaz11
if (i<ntrs) {
for (j in ((i+1):ntrs)) {
trans2 <- trs[j]
sf2 <- sf0[sf0$trans==trans2,]
jointt <- intersect(sf1$time,sf2$time)
if (length(jointt)>0) {
varHazij <- rep(NA,length(jointt))
ik <- match(jointt,sf1$time)
jk <- match(jointt,sf2$time)
varHazij <- cumsum(-sf1$noevent[ik]*sf2$noevent[jk]/sf1$norisk[ik]^3)
## sf1$norisk[ik] should be equal to sf2$norisk[jk]
varHaz12 <- varHaz[varHaz$trans1==trans1 & varHaz$trans2==trans2,]
varHaz12$varHaz <- NA
varHaz12$varHaz[match(jointt,allt)] <- varHazij
varHaz12$varHaz <- NAfix(varHaz12$varHaz,subst=0)
varHaz[varHaz$trans1==trans1 & varHaz$trans2==trans2,] <- varHaz12
}
}
}
}
}
}
}
}
else { # aalen
labels <- attr(Terms, "term.labels")
if (length(labels)==1) {
if (labels == "strata(trans)") { # no covariates, no call to C
sf0 <- summary(survfit(object))
norisk <- sf0$n.risk
noevent <- sf0$n.event
sf0 <- data.frame(time=sf0$time,Haz=-log(sf0$surv),norisk=norisk,
noevent=noevent,var=sf0$std.err^2/(sf0$surv)^2,trans=as.numeric(sf0$strata))
allt <- sort(unique(c(sf0$time,lasty)))
nt <- length(allt)
K <- max(sf0$trans)
### Set up empty structures for Haz and varHaz
Haz <- data.frame(time=rep(allt,K),Haz=NA,trans=rep(1:K,rep(nt,K)))
if (variance) {
tr12 <- data.frame(trans1=rep(1:K,rep(K,K)),trans2=rep(1:K,K))
tr12 <- tr12[tr12$trans1<=tr12$trans2,]
varHaz <- data.frame(time=rep(allt,K*(K+1)/2),varHaz=0,
trans1=rep(tr12$trans1,rep(nt,K*(K+1)/2)),
trans2=rep(tr12$trans2,rep(nt,K*(K+1)/2)))
}
for (k in 1:K) {
# no need for inner loop, since estimated hazards are uncorrelated
sfk <- sf0[sf0$trans==k,]
wht <- match(sfk$time,allt)
Hazk <- Haz[Haz$trans==k,]
Hazk$Haz[wht] <- sfk$Haz
Hazk$Haz <- NAfix(Hazk$Haz,subst=0)
Haz[Haz$trans==k,] <- Hazk
if (variance) {
varHazkk <- varHaz[varHaz$trans1==k & varHaz$trans2==k,]
varHazkk$varHaz <- NA
varHazkk$varHaz[wht] <- sfk$var
varHazkk$varHaz <- NAfix(varHazkk$varHaz,subst=0)
varHaz[varHaz$trans1==k & varHaz$trans2==k,] <- varHazkk
}
}
}
}
else {
method <- object$method
if (method=="breslow") method <- 1
else if (method=="efron") method <- 2
else stop("Only \"efron\" and \"breslow\" methods for ties supported")
# Copy of the data was already recreated. Finish the job for second purpose.
# Get the sort index for the data, and add a column to y if
# necessary to make it of the "counting process" type
# (only 1 C routine to handle both cases).
#
type <- attr(y, 'type')
if (type=='counting') {
if (has.strata) ord <- order(mf[,strat], y[,2], -y[,3])
else ord <- order(y[,2], -y[,3])
}
else if (type=='right') {
if (has.strata) ord <- order(mf[,strat], y[,1], -y[,2])
else ord <- order(y[,1], -y[,2])
miny <- min(y[,1])
if (miny < 0) y <- cbind(2*miny -1, y)
else y <- cbind(-1, y)
}
else stop("Cannot handle \"", type, "\" type survival data")
# Previously, in call to coxph.getdata(), x=variance was used as argument,
# meaning that data$x would be non-null iff variance=TRUE, hence the previous
# if (!is.null(data$x)) x <- data$x[ord,], is changed into: if (variance) ...
if (variance) x <- x[ord,]
else x <- 0
# The strata part works differently from survfit; each line in
# newdata is associated with a single stratum, explicitly given in
# newdata
if (has.strata) newstrat <- (as.numeric(mf[,strat]))[ord]
else newstrat <- rep(1,n)
newstrat <- cumsum(table(newstrat)) # !!! need to check what happens without strata
H <- length(newstrat)
subterms <- function(tt, i) {
dataClasses <- attr(tt, "dataClasses")
predvars <- attr(tt, "predvars")
oldnames <- dimnames(attr(tt, 'factors'))[[1]]
tt <- tt[i]
index <- match(dimnames(attr(tt, 'factors'))[[1]], oldnames)
if (length(index) >0) {
if (!is.null(predvars))
attr(tt, "predvars") <- predvars[c(1, index+1)]
if (!is.null(dataClasses))
attr(tt, "dataClasses") <- dataClasses[index]
}
tt
}
if (has.strata) {
temp <- untangle.specials(Terms, 'strata')
if (length(temp$vars))
Terms <- subterms(Terms, -temp$terms)
}
# Get the second, "new" data set
Terms2 <- delete.response(Terms)
if (has.strata) {
if (length(attr(Terms2, 'specials')$strata))
Terms2 <- subterms(Terms2, -attr(Terms2, 'specials')$strata)
if (!is.null(object$xlevels)) {
myxlev <- object$xlevels[match(attr(Terms2, "term.labels"),
names(object$xlevels), nomatch=0)]
if (length(myxlev)==0) myxlev <- NULL
}
else myxlev <- NULL
mf2 <- model.frame(Terms2, data=newdata, xlev=myxlev)
}
else mf2 <- model.frame(Terms2, data=newdata, xlev=object$xlevels)
offset2 <- 0 #offset variable for the new data set
if (!missing(newdata)) {
offset2 <- model.offset(mf2)
if (length(offset2) >0) offset2 <- offset2 - mean(offset)
else offset2 <- 0
x2 <- model.matrix(Terms2, mf2)[,-1, drop=FALSE] #no intercept
}
else stop("newdata missing")
if (has.strata & is.null(newdata$strata)) stop("no \"strata\" column present in newdata")
n2 <- nrow(x2)
# Compute risk scores for the new subjects
coef <- ifelse(is.na(object$coefficients), 0, object$coefficients)
newrisk <- exp(c(x2 %*% coef) + offset2 - sum(coef*object$means))
dimnames(y) <- NULL # I only use part of Y, so names become invalid
storage.mode(y) <- 'double'
ndead <- sum(y[,3])
untimes <- sort(unique(y[,2][y[,3]==1]))
nt <- length(untimes)
# n2,nvar are called K,p in 'agmssurv'
surv <- .C('agmssurv', ### modeled after 'agsurv2' from survival, changes indicated
sn = as.integer(n),
sp = as.integer(nvar),
svar = as.integer(variance),
smethod = as.integer(method),
sH = as.integer(H),
sK = as.integer(n2),
snt = as.integer(nt), ## NEW: length of unt
y = y[ord,],
score = as.double(score[ord]),
xmat = as.double(x),
varcov = as.double(object$var),
strata = as.integer(c(0,newstrat)), ### CHANGED: format to contain 0, end of stratum 1, end of stratum 2 etc in y
kstrata = as.integer(newdata$strata), ### NEW: kstrata[k] = stratum in newdata[k,]
unt = as.double(untimes), ### NEW: unique event times from all strata
newx = as.double(x2),
newrisk = as.double(newrisk),
Haz = double(nt*n2), ### NEW: OUTPUT, what used to be surv, dimension has changed
varHaz = double(nt*n2*(n2+1)/2), ### NEW: OUTPUT, what used to be varhaz, dimension has changed, also contains covariances
d = double(3*nvar), ### working vectors d
work = double(nt*n2*(nvar+1)) ### NEW: another working vector
)
# Restructure Haz and varHaz as data frames
Haz <- data.frame(time=rep(untimes,n2),Haz=surv$Haz,trans=rep(1:n2,rep(nt,n2)))
varHaz <- as.vector(t(matrix(surv$varHaz,ncol=nt))) # change row and column order
hlp <- matrix(c(rep(1:n2,rep(n2,n2)),rep(1:n2,n2)),n2^2,2)
hlp <- hlp[hlp[,1]<=hlp[,2],]
varHaz <- data.frame(time=rep(untimes,n2*(n2+1)/2),varHaz=varHaz,
trans1=rep(hlp[,1],rep(nt,n2*(n2+1)/2)),
trans2=rep(hlp[,2],rep(nt,n2*(n2+1)/2)))
# Squeeze in the lasty, unless lasty is event time
if (lasty > max(untimes)) {
Hmat <- matrix(Haz$Haz,nrow=nt)
Hmat <- rbind(Hmat,Hmat[nt,])
vHmat <- matrix(varHaz$varHaz,nrow=nt)
vHmat <- rbind(vHmat,vHmat[nt,])
untimes <- c(untimes,lasty)
nt <- nt+1
Haz <- data.frame(time=rep(untimes,n2),Haz=as.vector(Hmat),trans=rep(1:n2,rep(nt,n2)))
varHaz <- data.frame(time=rep(untimes,n2*(n2+1)/2),varHaz=as.vector(vHmat),
trans1=rep(hlp[,1],rep(nt,n2*(n2+1)/2)),
trans2=rep(hlp[,2],rep(nt,n2*(n2+1)/2)))
}
}
}
if (variance) res <- list(Haz=Haz,varHaz=varHaz,trans=trans)
else res <- list(Haz=Haz,trans=trans)
class(res) <- "msfit"
return(res)
}
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Defines functions `msfit`
#' Compute subject-specific transition hazards with (co-)variances
#'
#' This function computes subject-specific or overall cumulative transition
#' hazards for each of the possible transitions in the multi-state model. If
#' requested, also the variances and covariances of the estimated cumulative
#' transition hazards are calculated.
#'
#' The data frame needs to have one row for each transition in the multi-state
#' model. An additional column \code{strata} (numeric) is needed to describe
#' for each transition to which stratum it belongs. The name has to be
#' \code{strata}, even if in the original \code{coxph} call another variable
#' was used. For details refer to de Wreede, Fiocco & Putter (2010). So far,
#' the results have been checked only for the \code{"breslow"} method of
#' dealing with ties in \code{\link[survival:coxph]{coxph}}, so this is
#' recommended.
#'
#' @param object A \code{\link[survival:coxph]{coxph}} object describing the
#' fit of the multi-state model
#' @param newdata A data frame with the same variable names as those that
#' appear in the \code{coxph} formula. Its use is somewhat different from
#' \code{\link[survival:survfit]{survfit}}. See Details. The argument
#' \code{newdata} may be omitted only if the right hand side of the formula in
#' the \code{coxph} object is \code{~strata(trans)}
#' @param variance A logical value indicating whether the (co-)variances of the
#' subject-specific transition hazards should be computed. Default is
#' \code{TRUE}
#' @param vartype A character string specifying the type of variances to be
#' computed (so only needed if \code{variance}=\code{TRUE}). Possible values
#' are \code{"aalen"} or \code{"greenwood"}
#' @param trans Transition matrix describing the states and transitions in the
#' multi-state model. See \code{trans} in \code{\link{msprep}} for more
#' detailed information
#' @return An object of class \code{"msfit"}, which is a list containing
#' \item{Haz }{A data frame with \code{time}, \code{Haz}, \code{trans},
#' containing the estimated subject-specific hazards for each of the
#' transitions in the multi-state model} \item{varHaz }{A data frame with
#' \code{time}, \code{Haz}, \code{trans1}, \code{trans2} containing the
#' variances (\code{trans1}=\code{trans2}) and covariances
#' (\code{trans1}<\code{trans2}) of the estimated hazards. This element is only
#' returned when \code{variance}=\code{TRUE}} \item{trans}{The transition
#' matrix used}
#' @author Hein Putter \email{H.Putter@@lumc.nl}
#' @seealso \code{\link{plot.msfit}}
#' @references Putter H, Fiocco M, Geskus RB (2007). Tutorial in biostatistics:
#' Competing risks and multi-state models. \emph{Statistics in Medicine}
#' \bold{26}, 2389--2430.
#'
#' Therneau TM, Grambsch PM (2000). \emph{Modeling Survival Data: Extending the
#' Cox Model}. Springer, New York.
#'
#' de Wreede LC, Fiocco M, and Putter H (2010). The mstate package for
#' estimation and prediction in non- and semi-parametric multi-state and
#' competing risks models. \emph{Computer Methods and Programs in Biomedicine}
#' \bold{99}, 261--274.
#'
#' de Wreede LC, Fiocco M, and Putter H (2011). mstate: An R Package for the
#' Analysis of Competing Risks and Multi-State Models. \emph{Journal of
#' Statistical Software}, Volume 38, Issue 7.
#' @keywords survival
#' @examples
#'
#' # transition matrix for illness-death model
#' tmat <- trans.illdeath()
#' # data in wide format, for transition 1 this is dataset E1 of
#' # Therneau & Grambsch (2000)
#' tg <- data.frame(illt=c(1,1,6,6,8,9),ills=c(1,0,1,1,0,1),
#' dt=c(5,1,9,7,8,12),ds=c(1,1,1,1,1,1),
#' x1=c(1,1,1,0,0,0),x2=c(6:1))
#' # data in long format using msprep
#' tglong <- msprep(time=c(NA,"illt","dt"),status=c(NA,"ills","ds"),
#' data=tg,keep=c("x1","x2"),trans=tmat)
#' # events
#' events(tglong)
#' table(tglong$status,tglong$to,tglong$from)
#' # expanded covariates
#' tglong <- expand.covs(tglong,c("x1","x2"))
#' # Cox model with different covariate
#' cx <- coxph(Surv(Tstart,Tstop,status)~x1.1+x2.2+strata(trans),
#' data=tglong,method="breslow")
#' summary(cx)
#' # new data, to check whether results are the same for transition 1 as
#' # those in appendix E.1 of Therneau & Grambsch (2000)
#' newdata <- data.frame(trans=1:3,x1.1=c(0,0,0),x2.2=c(0,1,0),strata=1:3)
#' msfit(cx,newdata,trans=tmat)
#'
#' @export
`msfit` <-
function(object, newdata, variance=TRUE, vartype=c("aalen","greenwood"), trans)
{
### Modeled after survfit.coxph from the survival library by Therneau;
### indeed much of the code is just copied. Survfit is also called
### for vartype="greenwood".
### Stripped some of the features (cluster, individual) and limited the
### choice of other (type, vartype).
### Input:
### object: a coxph object
### newdata: a dataset with K lines for K transitions, as in survfit;
### newdata should contain a column "strata", specifying to which
### strata in the coxph object the K lines of newdata correspond.
### Argument newdata may only be omitted when the formula ends
### with ~strata(trans)
### variance: if TRUE (default), estimated (co)variance of cumulative
### hazards is returned
### vartype: aalen (default), greenwood is only supported in absence
### of newdata
### trans: the transition matrix of the multi-state model
if(!is.null((object$call)$weights) || !is.null(object$weights))
stop("msfit cannot (yet) compute the result for a weighted model")
Terms <- terms(object)
strat <- attr(Terms, "specials")$strata
cluster <- attr(Terms, "specials")$cluster
if (length(cluster)) stop("cluster terms are not supported")
if (!is.null(attr(object$terms, "specials")$tt))
stop("msfit cannot yet process coxph models with a tt term")
resp <- attr(Terms, "variables")[attr(Terms, "response")]
nvar <- length(object$coefficients)
score <- exp(object$linear.predictors) # exp(beta^T Z_i)
vartype <- match.arg(vartype)
if (is.na(vartype)) stop("Invalid variance type specified")
# Recreate a copy of the data (formerly using coxph.getdata, now with model.frame)
# Two purposes. One to find largest time point to be used in end result.
# Second later for call to C.
has.strata <- !is.null(attr(object$terms, 'specials')$strata)
if (is.null(object$y) || is.null(object[['x']]) ||
!is.null(object$call$weights) ||
(has.strata && is.null(object$strata)) ||
!is.null(attr(object$terms, 'offset'))) {
mf <- model.frame(object)
}
else mf <- NULL #useful for if statements later
if (is.null(mf)) y <- object[['y']]
else {
y <- model.response(mf)
y2 <- object[['y']]
if (!is.null(y2) && any(dim(y2) != dim(y)))
stop("Could not reconstruct the y vector")
}
if (is.null(object[['x']])) x <- model.matrix(object, data=mf)
else x <- object[['x']]
n <- nrow(y)
if (n != object$n[1] || nrow(x) !=n)
stop("Failed to reconstruct the original data set")
# Here we find largest time point
type <- attr(y, 'type')
if (type=='counting') lasty <- max(y[,2]) # start, stop, status
else if (type=='right') lasty <- max(y[,1]) # time, status
else stop("Cannot handle \"", type, "\" type survival data")
if (is.null(mf)) offset <- 0
else {
offset <- model.offset(mf)
if (is.null(offset)) offset <- 0
}
Terms <- object$terms
if (!has.strata) strata <- rep(0L,n)
else {
stangle <- untangle.specials(Terms, 'strata') # used multiple times
strata <- object$strata #try this first
if (is.null(strata)){
if (length(stangle$vars)==1) strata <- mf[[stangle$vars]]
else strata <- strata(mf[, stangle$vars], shortlabel=TRUE)
}
}
if (has.strata) {
temp <- attr(Terms, "specials")$strata
factors <- attr(Terms, "factors")[temp,]
strata.interaction <- any(t(factors)*attr(Terms, "order") >1)
if (strata.interaction) stop("Interaction terms with strata not supported")
}
if (vartype=="greenwood") {
if (missing(trans))
stop("argument trans missing; needed for vartype=\"greenwood\"")
labels <- attr(Terms, "term.labels")
if (length(labels)!=1) stop("Invalid formula for greenwood, ~strata(trans) needed, no covariates allowed")
if (attr(Terms, "term.labels") != "strata(trans)") # This will happen if length=1 and different from strata(trans)
stop("Invalid formula for greenwood, ~strata(trans) needed, no covariates allowed")
sf0 <- summary(survfit(object))
norisk <- sf0$n.risk
noevent <- sf0$n.event
sf0 <- data.frame(time=sf0$time,Haz=-log(sf0$surv),norisk=norisk,
noevent=noevent, trans=as.numeric(sf0$strata))
allt <- sort(unique(c(sf0$time,lasty)))
nt <- length(allt)
K <- nrow(to.trans2(trans))
### Set up empty structures for Haz and varHaz
Haz <- data.frame(time=rep(allt,K),Haz=NA,trans=rep(1:K,rep(nt,K)))
if (variance) {
tr12 <- data.frame(trans1=rep(1:K,rep(K,K)),trans2=rep(1:K,K))
tr12 <- tr12[tr12$trans1<=tr12$trans2,]
varHaz <- data.frame(time=rep(allt,K*(K+1)/2),varHaz=0,
trans1=rep(tr12$trans1,rep(nt,K*(K+1)/2)),
trans2=rep(tr12$trans2,rep(nt,K*(K+1)/2)))
}
### Easiest to loop over starting states
S <- nrow(trans)
for (s in 1:S) {
trs <- trans[s,]
trs <- trs[!is.na(trs)]
ntrs <- length(trs)
if (ntrs > 0) {
for (i in 1:ntrs) {
trans1 <- trs[i]
sf1 <- sf0[sf0$trans==trans1,]
Haz$Haz[(trans1-1)*nt + match(sf1$time,allt)] <- sf1$Haz
Haz$Haz[(trans1-1)*nt + 1:nt] <- NAfix(Haz$Haz[(trans1-1)*nt + 1:nt],subst=0)
if (variance) {
varHaz1 <- cumsum((sf1$norisk-sf1$noevent)*sf1$noevent/sf1$norisk^3)
varHaz11 <- varHaz[varHaz$trans1==trans1 & varHaz$trans2==trans1,]
varHaz11$varHaz <- NA
varHaz11$varHaz[match(sf1$time,allt)] <- varHaz1
varHaz11$varHaz <- NAfix(varHaz11$varHaz,subst=0)
varHaz[varHaz$trans1==trans1 & varHaz$trans2==trans1,] <- varHaz11
if (i<ntrs) {
for (j in ((i+1):ntrs)) {
trans2 <- trs[j]
sf2 <- sf0[sf0$trans==trans2,]
jointt <- intersect(sf1$time,sf2$time)
if (length(jointt)>0) {
varHazij <- rep(NA,length(jointt))
ik <- match(jointt,sf1$time)
jk <- match(jointt,sf2$time)
varHazij <- cumsum(-sf1$noevent[ik]*sf2$noevent[jk]/sf1$norisk[ik]^3)
## sf1$norisk[ik] should be equal to sf2$norisk[jk]
varHaz12 <- varHaz[varHaz$trans1==trans1 & varHaz$trans2==trans2,]
varHaz12$varHaz <- NA
varHaz12$varHaz[match(jointt,allt)] <- varHazij
varHaz12$varHaz <- NAfix(varHaz12$varHaz,subst=0)
varHaz[varHaz$trans1==trans1 & varHaz$trans2==trans2,] <- varHaz12
}
}
}
}
}
}
}
}
else { # aalen
labels <- attr(Terms, "term.labels")
if (length(labels)==1) {
if (labels == "strata(trans)") { # no covariates, no call to C
sf0 <- summary(survfit(object))
norisk <- sf0$n.risk
noevent <- sf0$n.event
sf0 <- data.frame(time=sf0$time,Haz=-log(sf0$surv),norisk=norisk,
noevent=noevent,var=sf0$std.err^2/(sf0$surv)^2,trans=as.numeric(sf0$strata))
allt <- sort(unique(c(sf0$time,lasty)))
nt <- length(allt)
K <- max(sf0$trans)
### Set up empty structures for Haz and varHaz
Haz <- data.frame(time=rep(allt,K),Haz=NA,trans=rep(1:K,rep(nt,K)))
if (variance) {
tr12 <- data.frame(trans1=rep(1:K,rep(K,K)),trans2=rep(1:K,K))
tr12 <- tr12[tr12$trans1<=tr12$trans2,]
varHaz <- data.frame(time=rep(allt,K*(K+1)/2),varHaz=0,
trans1=rep(tr12$trans1,rep(nt,K*(K+1)/2)),
trans2=rep(tr12$trans2,rep(nt,K*(K+1)/2)))
}
for (k in 1:K) {
# no need for inner loop, since estimated hazards are uncorrelated
sfk <- sf0[sf0$trans==k,]
wht <- match(sfk$time,allt)
Hazk <- Haz[Haz$trans==k,]
Hazk$Haz[wht] <- sfk$Haz
Hazk$Haz <- NAfix(Hazk$Haz,subst=0)
Haz[Haz$trans==k,] <- Hazk
if (variance) {
varHazkk <- varHaz[varHaz$trans1==k & varHaz$trans2==k,]
varHazkk$varHaz <- NA
varHazkk$varHaz[wht] <- sfk$var
varHazkk$varHaz <- NAfix(varHazkk$varHaz,subst=0)
varHaz[varHaz$trans1==k & varHaz$trans2==k,] <- varHazkk
}
}
}
}
else {
method <- object$method
if (method=="breslow") method <- 1
else if (method=="efron") method <- 2
else stop("Only \"efron\" and \"breslow\" methods for ties supported")
# Copy of the data was already recreated. Finish the job for second purpose.
# Get the sort index for the data, and add a column to y if
# necessary to make it of the "counting process" type
# (only 1 C routine to handle both cases).
#
type <- attr(y, 'type')
if (type=='counting') {
if (has.strata) ord <- order(mf[,strat], y[,2], -y[,3])
else ord <- order(y[,2], -y[,3])
}
else if (type=='right') {
if (has.strata) ord <- order(mf[,strat], y[,1], -y[,2])
else ord <- order(y[,1], -y[,2])
miny <- min(y[,1])
if (miny < 0) y <- cbind(2*miny -1, y)
else y <- cbind(-1, y)
}
else stop("Cannot handle \"", type, "\" type survival data")
# Previously, in call to coxph.getdata(), x=variance was used as argument,
# meaning that data$x would be non-null iff variance=TRUE, hence the previous
# if (!is.null(data$x)) x <- data$x[ord,], is changed into: if (variance) ...
if (variance) x <- x[ord,]
else x <- 0
# The strata part works differently from survfit; each line in
# newdata is associated with a single stratum, explicitly given in
# newdata
if (has.strata) newstrat <- (as.numeric(mf[,strat]))[ord]
else newstrat <- rep(1,n)
newstrat <- cumsum(table(newstrat)) # !!! need to check what happens without strata
H <- length(newstrat)
subterms <- function(tt, i) {
dataClasses <- attr(tt, "dataClasses")
predvars <- attr(tt, "predvars")
oldnames <- dimnames(attr(tt, 'factors'))[[1]]
tt <- tt[i]
index <- match(dimnames(attr(tt, 'factors'))[[1]], oldnames)
if (length(index) >0) {
if (!is.null(predvars))
attr(tt, "predvars") <- predvars[c(1, index+1)]
if (!is.null(dataClasses))
attr(tt, "dataClasses") <- dataClasses[index]
}
tt
}
if (has.strata) {
temp <- untangle.specials(Terms, 'strata')
if (length(temp$vars))
Terms <- subterms(Terms, -temp$terms)
}
# Get the second, "new" data set
Terms2 <- delete.response(Terms)
if (has.strata) {
if (length(attr(Terms2, 'specials')$strata))
Terms2 <- subterms(Terms2, -attr(Terms2, 'specials')$strata)
if (!is.null(object$xlevels)) {
myxlev <- object$xlevels[match(attr(Terms2, "term.labels"),
names(object$xlevels), nomatch=0)]
if (length(myxlev)==0) myxlev <- NULL
}
else myxlev <- NULL
mf2 <- model.frame(Terms2, data=newdata, xlev=myxlev)
}
else mf2 <- model.frame(Terms2, data=newdata, xlev=object$xlevels)
offset2 <- 0 #offset variable for the new data set
if (!missing(newdata)) {
offset2 <- model.offset(mf2)
if (length(offset2) >0) offset2 <- offset2 - mean(offset)
else offset2 <- 0
x2 <- model.matrix(Terms2, mf2)[,-1, drop=FALSE] #no intercept
}
else stop("newdata missing")
if (has.strata & is.null(newdata$strata)) stop("no \"strata\" column present in newdata")
n2 <- nrow(x2)
# Compute risk scores for the new subjects
coef <- ifelse(is.na(object$coefficients), 0, object$coefficients)
newrisk <- exp(c(x2 %*% coef) + offset2 - sum(coef*object$means))
dimnames(y) <- NULL # I only use part of Y, so names become invalid
storage.mode(y) <- 'double'
ndead <- sum(y[,3])
untimes <- sort(unique(y[,2][y[,3]==1]))
nt <- length(untimes)
# n2,nvar are called K,p in 'agmssurv'
surv <- .C('agmssurv', ### modeled after 'agsurv2' from survival, changes indicated
sn = as.integer(n),
sp = as.integer(nvar),
svar = as.integer(variance),
smethod = as.integer(method),
sH = as.integer(H),
sK = as.integer(n2),
snt = as.integer(nt), ## NEW: length of unt
y = y[ord,],
score = as.double(score[ord]),
xmat = as.double(x),
varcov = as.double(object$var),
strata = as.integer(c(0,newstrat)), ### CHANGED: format to contain 0, end of stratum 1, end of stratum 2 etc in y
kstrata = as.integer(newdata$strata), ### NEW: kstrata[k] = stratum in newdata[k,]
unt = as.double(untimes), ### NEW: unique event times from all strata
newx = as.double(x2),
newrisk = as.double(newrisk),
Haz = double(nt*n2), ### NEW: OUTPUT, what used to be surv, dimension has changed
varHaz = double(nt*n2*(n2+1)/2), ### NEW: OUTPUT, what used to be varhaz, dimension has changed, also contains covariances
d = double(3*nvar), ### working vectors d
work = double(nt*n2*(nvar+1)) ### NEW: another working vector
)
# Restructure Haz and varHaz as data frames
Haz <- data.frame(time=rep(untimes,n2),Haz=surv$Haz,trans=rep(1:n2,rep(nt,n2)))
varHaz <- as.vector(t(matrix(surv$varHaz,ncol=nt))) # change row and column order
hlp <- matrix(c(rep(1:n2,rep(n2,n2)),rep(1:n2,n2)),n2^2,2)
hlp <- hlp[hlp[,1]<=hlp[,2],]
varHaz <- data.frame(time=rep(untimes,n2*(n2+1)/2),varHaz=varHaz,
trans1=rep(hlp[,1],rep(nt,n2*(n2+1)/2)),
trans2=rep(hlp[,2],rep(nt,n2*(n2+1)/2)))
# Squeeze in the lasty, unless lasty is event time
if (lasty > max(untimes)) {
Hmat <- matrix(Haz$Haz,nrow=nt)
Hmat <- rbind(Hmat,Hmat[nt,])
vHmat <- matrix(varHaz$varHaz,nrow=nt)
vHmat <- rbind(vHmat,vHmat[nt,])
untimes <- c(untimes,lasty)
nt <- nt+1
Haz <- data.frame(time=rep(untimes,n2),Haz=as.vector(Hmat),trans=rep(1:n2,rep(nt,n2)))
varHaz <- data.frame(time=rep(untimes,n2*(n2+1)/2),varHaz=as.vector(vHmat),
trans1=rep(hlp[,1],rep(nt,n2*(n2+1)/2)),
trans2=rep(hlp[,2],rep(nt,n2*(n2+1)/2)))
}
}
}
if (variance) res <- list(Haz=Haz,varHaz=varHaz,trans=trans)
else res <- list(Haz=Haz,trans=trans)
class(res) <- "msfit"
return(res)
}
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