R/riskRegression.R

Defines functions riskRegression

Documented in riskRegression

#' Risk Regression
#' Fits a regression model for the risk of an event -- allowing for competing
#' risks.
#' 
#' This is a wrapper for the function \code{comp.risk} from the timereg package.
#' The main difference is one marks variables in the formula that should have a
#' time-dependent effect whereas in \code{comp.risk} one marks variables that
#' should have a time constant (proportional) effect.
#' @aliases riskRegression ARR LRR
#' @param formula Formula where the left hand side specifies the event
#' history event.history and the right hand side the linear predictor.  See
#' examples.
#' @param data The data for fitting the model in which includes all
#' the variables included in formula.
#' @param times Vector of times. For each time point in \code{times}
#' estimate the baseline risk and the timevarying coefficients.
#' @param link \code{"relative"} for the absolute risk regression
#' model.  \code{"logistic"} for the logistic risk regression model.
#' \code{"prop"} for the Fine-Gray regression model.
#' @param cause The cause of interest.
#' @param conf.int If \code{TRUE} return the iid decomposition, that
#' can be used to construct confidence bands for predictions.
#' @param cens.model Specified the model for the (conditional)
#' censoring distribution used for deriving weights (IFPW). Defaults
#' to "KM" (the Kaplan-Meier method ignoring covariates) alternatively
#' it may be "Cox" (Cox regression).
#' @param cens.formula Right hand side of the formula used for fitting
#' the censoring model.  If not specified the right hand side of
#' \code{formula} is used.
#' @param max.iter Maximal number of iterations.
#' @param conservative If \code{TRUE} use variance formula that ignores the contribution
#' by the estimate of the inverse of the probability of censoring weights 
#' @param ... Further arguments passed to \code{comp.risk}
#' @author Thomas A. Gerds \email{tag@@biostat.ku.dk}, Thomas H. Scheike \email{ts@@biostat.ku.dk}
#' @references
#' Thomas A Gerds, Thomas H Scheike, and Per K Andersen. Absolute risk
#' regression for competing risks: interpretation, link functions, and
#' prediction. Statistics in medicine, 31(29):3921--3930, 2012.
#'
#' Scheike, Zhang and Gerds (2008), Predicting cumulative incidence probability
#' by direct binomial regression, Biometrika, 95, 205-220.
#' 
#' Scheike and Zhang (2007), Flexible competing risks regression modelling and
#' goodness of fit, LIDA, 14, 464-483.
#' 
#' Martinussen and Scheike (2006), Dynamic regression models for survival data,
#' Springer.
##' @examples
##' 
##' library(prodlim)
##' data(Melanoma,package="riskRegression")
##' ## tumor thickness on the log-scale
##' Melanoma$logthick <- log(Melanoma$thick)
##' 
##' # Single binary factor
##' 
##' ## absolute risk regression
##' library(survival)
##' library(prodlim)
##' fit.arr <- ARR(Hist(time,status)~sex,data=Melanoma,cause=1)
##' print(fit.arr)
##' # show predicted cumulative incidences
##' plot(fit.arr,col=3:4,newdata=data.frame(sex=c("Female","Male")))
##' 
##' ## compare with non-parametric Aalen-Johansen estimate
##' library(prodlim)
##' fit.aj <- prodlim(Hist(time,status)~sex,data=Melanoma)
##' plot(fit.aj,conf.int=FALSE)
##' plot(fit.arr,add=TRUE,col=3:4,newdata=data.frame(sex=c("Female","Male")))
##' 
##' ## with time-dependent effect
##' fit.tarr <- ARR(Hist(time,status)~strata(sex),data=Melanoma,cause=1)
##' plot(fit.tarr,newdata=data.frame(sex=c("Female","Male")))
##' 
##' ## logistic risk regression
##' fit.lrr <- LRR(Hist(time,status)~sex,data=Melanoma,cause=1)
##' summary(fit.lrr)
##' 
##' 
##' # Single continuous factor
##' 
##' ## tumor thickness on the log-scale
##' Melanoma$logthick <- log(Melanoma$thick)
##' 
##' ## absolute risk regression 
##' fit2.arr <- ARR(Hist(time,status)~logthick,data=Melanoma,cause=1)
##' print(fit2.arr)
##' # show predicted cumulative incidences
##' plot(fit2.arr,col=1:5,newdata=data.frame(logthick=quantile(Melanoma$logthick)))
##' 
##' ## comparison with nearest neighbor non-parametric Aalen-Johansen estimate
##' library(prodlim)
##' fit2.aj <- prodlim(Hist(time,status)~logthick,data=Melanoma)
##' plot(fit2.aj,conf.int=FALSE,newdata=data.frame(logthick=quantile(Melanoma$logthick)))
##' plot(fit2.arr,add=TRUE,col=1:5,lty=3,newdata=data.frame(logthick=quantile(Melanoma$logthick)))
##' 
##' ## logistic risk regression
##' fit2.lrr <- LRR(Hist(time,status)~logthick,data=Melanoma,cause=1)
##' summary(fit2.lrr)
##' 
##' ## change model for censoring weights
##' library(rms)
##' fit2a.lrr <- LRR(Hist(time,status)~logthick,
##'                  data=Melanoma,
##'                  cause=1,
##'                  cens.model="cox",
##'                  cens.formula=~sex+epicel+ulcer+age+logthick)
##' summary(fit2a.lrr)
##' 
##' ##  compare prediction performance
##' Score(list(ARR=fit2.arr,AJ=fit2.aj,LRR=fit2.lrr),formula=Hist(time,status)~1,data=Melanoma)
##' 
##' 
##' # multiple regression
##' library(riskRegression)
##' library(prodlim)
##' # absolute risk model
##' multi.arr <- ARR(Hist(time,status)~logthick+sex+age+ulcer,data=Melanoma,cause=1)
##' 
##' # stratified model allowing different baseline risk for the two gender
##' multi.arr <- ARR(Hist(time,status)~thick+strata(sex)+age+ulcer,data=Melanoma,cause=1)
##' 
##' # stratify by a continuous variable: strata(age)
##' multi.arr <- ARR(Hist(time,status)~tp(thick,power=0)+strata(age)+sex+ulcer,
##'                  data=Melanoma,
##'                  cause=1)
##' 
##' fit.arr2a <- ARR(Hist(time,status)~tp(thick,power=1),data=Melanoma,cause=1)
##' summary(fit.arr2a)
##' fit.arr2b <- ARR(Hist(time,status)~timevar(thick),data=Melanoma,cause=1)
##' summary(fit.arr2b)
##' 
##' ## logistic risk model
##' fit.lrr <- LRR(Hist(time,status)~thick,data=Melanoma,cause=1)
##' summary(fit.lrr)
##' 
##' 
##' 
##' 
##' 
##' ## nearest neighbor non-parametric Aalen-Johansen estimate
##' library(prodlim)
##' fit.aj <- prodlim(Hist(time,status)~thick,data=Melanoma)
##' plot(fit.aj,conf.int=FALSE)
##' 
##' # prediction performance
##' x <- Score(list(fit.arr2a,fit.arr2b,fit.lrr),
##'              data=Melanoma,
##'              formula=Hist(time,status)~1,
##'              cause=1,
##'              split.method="none")
##'
##' 
#' @keywords survival
#' @export
riskRegression <- function(formula,
                           data,
                           times,
                           link="relative",
                           cause,
                           conf.int=TRUE,
                           cens.model,
                           cens.formula,
                           max.iter=50,
                           conservative=TRUE,
                           ...){
    # {{{ preliminaries
    weighted=0
    detail=0
    stopifnot(is.numeric(max.iter)&&max.iter[[1]]>0&&(round(max.iter[[1]])==max.iter[[1]]))
    # trans=1 P_1=1-exp(- ( x' b(b)+ z' gam t^time.pow) ), 
    # trans=2 P_1=1-exp(-exp(x a(t)+ z` b )
    # trans=not done P_1=1-exp(-x a(t) exp(z` b )) is not good numerically
    # trans=3 P_1=exp(-exp(x a(t)+ z` b )
    ##   trans <- switch(link,"log"=
    trans <- switch(link,
                    "additive"="additive", # 
                    "prop"="prop",     # Proportional hazards (Cox, FG)
                    "logistic"="logistic2", # Logistic absolute risks
                    "relative"="rcif2") # Relative absolute risks
    # }}}
    # {{{ read the data and the design
    call <- match.call()
    EHF <- prodlim::EventHistory.frame(formula,
                                       data,
                                       specials=c("timevar","strata","prop","const","tp"),
                                       stripSpecials=c("timevar","prop"),
                                       stripArguments=list("prop"=list("power"=0),"timevar"=list("test"=0)),
                                       stripAlias=list("timevar"=c("strata"),"prop"=c("tp","const")),
                                       stripUnspecials="prop",
                                       specialsDesign=TRUE,
                                       dropIntercept=TRUE)
    ## Terms <- attr(EHF,"Terms")
    Terms <- terms(formula)
    event.history <- EHF$event.history
    n <- NROW(event.history)
    # }}}
    # {{{ time-constant design matrix Z
    ## Specials <- attr(Terms,"stripped.specials")
    ## strippedArguments <- attr(Terms,"stripped.arguments")
    Z <- EHF$prop
    if (!is.null(Z)){
        power.args <- attr(EHF$prop,"arguments.terms")$power
        stopifnot(all(match(colnames(Z),names(power.args),nomatch=0)))
        timeconst.power <- as.numeric(power.args[colnames(Z)])
        factorLevelsZ <- attr(EHF$prop,"levels")
        refLevelsZ <- lapply(factorLevelsZ,function(x)x[1])
        colnamesZ <- colnames(Z)
        dimZ <- NCOL(Z)
        fixed <- 1
        names(timeconst.power) <- colnames(Z)
        stopifnot(length(timeconst.power)==dimZ)
        if (!(all(timeconst.power %in% 0:2)))
            stop("Only powers of time in 0,1,2 can be multipled to constant covariates")
    } else{
        Z <- matrix(0,n,1)
        dimZ <- 1
        colnamesZ <- NULL
        fixed <- 0
        factorLevelsZ <- NULL
        refLevelsZ <- NULL
        timeconst.power <- NULL
    }
    # }}}
    # {{{ time-varying design matrix X
    X <- EHF$timevar
    if (!is.null(X)){
        test.args <- attr(EHF$timevar,"arguments.terms")$test
        stopifnot(all(match(colnames(X),names(test.args),nomatch=0)))
        timevar.test <- as.numeric(test.args[colnames(X)])
        names(timevar.test) <- colnames(X)
        factorLevelsX <- attr(EHF$timevar,"levels")
        refLevelsX <- lapply(factorLevelsX,function(x)x[1])
        ## intercept 
        X <- cbind("Intercept"=rep(1,n),X)
        timevar.test <- c(0,as.numeric(timevar.test))
        dimX <- NCOL(X)
    } else{
        ## X <- matrix(0,n,1)
        dimX <- 1
        colnamesX <- NULL
        factorLevelsX <- NULL
        refLevelsX <- NULL
        timevar.test <- 0
        ## intercept 
        X <- cbind("Intercept"=rep(1,n))
    }
    stopifnot(length(timevar.test)==dimX)
    colnamesX <- colnames(X)
    if (!(all(timevar.test %in% 0:2)))
        stop("Time power tests only available for powers 0,1,2")
    theData <- data.frame(cbind(unclass(event.history),do.call("cbind",EHF[-1])))
    # }}}
    # {{{ event.history and order the data
    delayed <- !(is.null(attr(event.history,"entry.type"))) && !(attr(EHF$event.history,"entry.type")=="")
    if (delayed){
        stop("Delayed entry is not (not yet) supported.")
    }
    cens.code <- attr(event.history,"cens.code")
    model.type <- attr(event.history,"model")
    states <- prodlim::getStates(event.history)
    stopifnot(model.type %in% c("survival","competing.risks"))
    cens.type <- attr(event.history,"cens.type")
    stopifnot(cens.type %in% c("rightCensored","uncensored"))
    neworder <- order(event.history[,"time"],-event.history[,"status"])
    event.history <- event.history[neworder,,drop=FALSE]
    Z <- Z[neworder,,drop=FALSE]
    X <- X[neworder,,drop=FALSE]
    theData <- theData[neworder,]
    if (model.type[[1]]!="survival" && !("event" %in% colnames(event.history)))
        warning("Only one cause of failure found in data.")
    eventtime <- as.vector(event.history[,"time"])
    time  <- numeric(length(eventtime))
    if (model.type %in% c("competing.risks","survival")){
        if (cens.type %in% c("rightCensored","uncensored")){
            delta <- as.vector(event.history[,"status"])
            if (model.type=="competing.risks"){
                if (missing(cause)) {
                    cause <- states[1]
                    warning(paste("Argument cause is missing, analysing cause 1: ",cause,". Other causes are:",paste(states[-1],collapse=","),sep=""))
                }else {
                    if (!(cause %in% states)) stop(paste("Cause",cause," is not among the causes in data; these are:",paste(states,collapse=",")))
                    cause <- match(cause,states,nomatch=0)
                }
                ## event is 1 if the event of interest occured and 0 otherwise
                event <-  event.history[,"event"] == cause
                if (sum(event)==0) stop(paste("No events of type:", cause, "in data."))
            } else{
                event <- delta
            }
        }else{
            stop("Works only for right-censored data")
        }
    }else{stop("Response is neither competing risks nor survival.")}
    # }}}
    # {{{ cluster variable
    clusters <- EHF$cluster
    if(is.null(clusters)){
        clusters  <-  0:(NROW(X) - 1)
        antclust  <-  NROW(X)
    } else {
        clusters  <-  as.integer(factor(clusters))-1
        antclust  <-  length(unique(clusters))
    }
    # }}}
    # {{{ time points for timevarametric components
    if (missing(times) || length(times)==0) {
        times <- NULL
    }
    else{
        times <- sort(unique(times))
    }
    ntimes <- length(times)
    # }}}
    # {{{ ipcw model
    if (missing(cens.model)) cens.model <- "KM"
    if (length(grep("^km|^kaplan|^marg",cens.model,ignore.case=TRUE))>0)
        cens.model <- "KM"
    else cens.model <- "cox"
    # }}}
    enames <- colnames(event.history)
    if ("event"%in%enames){# competing risks
        event.history[event.history[,"status"]==0,"event"] <- 0
        event.history <- event.history[,-match("status",enames)]
    }else{
        colnames(event.history) <- sub("status","event",colnames(event.history))
    }
    if ("entry"%in%enames){ # delayed entry
        timeregformula <- "timereg::Event(entry,time,event)"
    }else{
        timeregformula <- "timereg::Event(time,event)"
    }
    if ((ipos <- match("Intercept",colnamesX,nomatch=0))>0)
        timeregformula <- paste0(timeregformula,"~+1")
    else
        timeregformula <- paste0(timeregformula,"~-1")
    if (length(colnamesZ)>0){
        const <- timereg::const
        timeregformula <- paste0(timeregformula,
                                 "+",
                                 paste0(sapply(colnamesZ,function(z){paste0("const(",z,")")}),collapse="+"))
    }
    if (length(colnamesX[-ipos])>0){
        trdat <- data.frame(event.history,Z,X[,-ipos,drop=FALSE])
        timeregformula <- paste0(timeregformula,"+",paste0(colnamesX[-ipos],collapse="+"))
    }else{
        trdat <- data.frame(event.history,Z)
    }
    Surv <- survival::Surv
    out <- timereg::comp.risk(as.formula(timeregformula),
                              data=trdat,
                              model=trans,
                              time.pow=timeconst.power,
                              time.pow.test=timevar.test,
                              times=times,
                              cause=cause,...)
    # }}}
    # {{{ prepare the output

    if (fixed==1){
        timeConstantCoef <- c(out$gamma)
        names(timeConstantCoef) <- colnamesZ
        timeConstantVar <- matrix(out$var.gamma,dimZ,dimZ,dimnames=list(colnamesZ,colnamesZ))
    }
    else{
        timeConstantCoef <- NULL
        timeConstantVar <- NULL
    }
    timeVaryingCoef <- out$cum
    timeVaryingVar <- out$var.cum
    score <- out$score
    timeConstantEffects <- list(coef=timeConstantCoef,var=timeConstantVar)
    class(timeConstantEffects) <- "timeConstantEffects"
    timeVaryingEffects <- list(coef=timeVaryingCoef,
                               var=timeVaryingVar)
    attr(timeConstantEffects,"variables") <- names(factorLevelsZ)
    attr(timeVaryingEffects,"variables") <- names(factorLevelsX)
    class(timeVaryingEffects) <- "timeVaryingEffects"
    out <- list(call=call,
                response=event.history,
                design=list(Terms=Terms,
                            const=colnamesZ,
                            timevar=colnamesX,
                            timepower=timeconst.power),
                link=link,
                time=times,
                timeConstantEffects=timeConstantEffects,
                timeconst.power=timeconst.power,
                timeVaryingEffects=timeVaryingEffects,
                score=score,
                cens.model= cens.model,
                factorLevels=c(factorLevelsX,factorLevelsZ),
                refLevels=c(refLevelsX,refLevelsZ),
                "na.action"=attr(EHF,"na.action"))
    if (is.null(out$call$cause))
        out$call$cause <- cause
    class(out) <- "riskRegression"
    return(out)
    # }}}
}

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riskRegression documentation built on Jan. 13, 2021, 11:12 a.m.