R/pred.ah.R

In addhazard: Fit Additive Hazards Models for Survival Analysis

#' Prediction Based on the Fitted Additive Hazards Model
#'
#' This function predicts a subject's overall hazard rates at given time points
#' based on this subject's covariate values.  The prediction function is an additive
#' hazards model fitted using \code{\link{ah}}.
#'
#' @param object an object of class inhering from \code{\link{ah}}.
#' @param newdata  a dataframe of an individual's predictors.
#' @param newtime  a given sequence of time points at which the prediction is performed.
#'  The time should be on the same scale as the survival time in \code{\link[survival]{Surv}}.
#' @param ...  further arguments passed to or from other methods.
#'
#' @return A dataframe including the time points for prediction, predicted values and their standard errors.
#'
#' @seealso \code{\link{ah}} for fitting the additive hazards model, \code{\link{nwtsco}} for
#' the description of nwtsco dataset
#'
#' @importFrom survival Surv
#' @importFrom stats delete.response model.matrix terms
#' @export
#'
#' @examples
#' library(survival)
#' ###  fit the additive hazards model to the data
#' nwts<- nwtsco[1:100,]
#' fit <- ah(Surv(trel,relaps) ~ age + instit, data = nwts,  ties = FALSE, robust = FALSE)
#'
#' ###  see the covariate names in the prediction function
#' fit$call
#' ###  the newdata should be a dataframe
#' ###  the variable names are the same as the covariate names in
#' ###  the prediction function
#' newdata <- data.frame(age=60, instit =1)
#'
#' ###  an alternative way to give the newdata
#' newdata <- nwtsco[101,]
#'
#' ###  based on this subject's covariate values, the function predicts  individual specific
#' ###  hazard rates at time points 3 and 5
#' predict(fit, newdata, newtime = c(3,5))
predict.ah <- function(object, newdata, newtime, ...) {

    # get all the middle step results obtained in the model
    # fitting step
    ingr <- ingredients(object)

    # names(ingredients.list) [1] 'Z' 't.fail' 'censor' 'n.obs'
    # 'wts' [6] 't.unique' 't.diff' 't1.unique' 'n.par' 'theta'
    # [11] 'effects' 'eta.ncol' 'match.eta' 'eta' 'eta.den.vec'
    # [16] 'eta.cum' 'idx.c' 'match.event' 'n.death' 'z.death'
    # [21] 'dLambda1' 'lambda0' 'Lambda0'

    tt <- terms(object)
    if (!inherits(object, "ah"))
        warning("calling predict.ah(<fake-ah-object>) ...")
    # if (missing(newdata) || is.null(newdata)){ pred <-
    # object$data$X #predictor matrix pred <- as.matrix(pred)
    # }else{ retrieve the columns in newdata that match the
    # names of the predictors in the model

    Terms <- delete.response(tt)
    # identify factor variable
    factor.var.list <- names(object$model)[sapply(object$model,
        is.factor) == T]
    # assign levels to newdata variable according to the
    # original data
    if (length(factor.var.list) != 0) {
        for (i in 1:length(factor.var.list)) {
            factor.var <- factor.var.list[i]
            newdata[[factor.var]] <- factor(newdata[[factor.var]],
                levels = levels(object$model[[factor.var]]))
        }
    }

    pred <- model.matrix(Terms, newdata)
    # contrasts.arg = lapply(newdata[sapply(newdata, is.factor)
    # ], constrats, , contrasts=FALSE
    pred <- as.numeric(pred[, -1])

    # predicted additive effect
    effect <- sum(pred * object$coef)

    # middle step results for obtaining predicted outcomes and
    # variances b <- do.call('cook.basics',ingredients.list)

    # cumulated baseline hazards Lambda0 <- b$Lambda0
    # search the location of the newtime in the original
    # timelines in the fitted model
    t.pos <- NULL
    t.unique <- ingr$t.unique

    # Store the predicted value
    L <- NULL

    for (i in 1:length(newtime)) {
        # newtime s is less than t_1, then we report
        if (newtime[i] < t.unique[1]) {
            print(paste("The data used for building the ah model does not have enough
                          information for predicting such a small t, t=",
                        newtime[i], "."))
            print(paste("remove, t=", newtime[i], " and run the prediction again"))
        } else {

            # find the position of newtime[i] on the original timeline
            c <- rank(c(newtime[i], t.unique), ties.method = "max")[1]
            # when newtime s equals one of the t.unique t_k, the above
            # rank function returns k+1 on the original timelines when
            # newtime s in between t_k and t_{k+1}, it returns k+1 on
            # the original timelines when newtime s = t_1, it returns 1
            # thus
            t.pos[i] <- ifelse(c == 1, 1, c - 1)
            # The predicted valued the additive part I
            L[i] <- effect * newtime[i] + ingr$Lambda0[t.pos[i]]

        }
    }
    L.var <- NULL
    if (!object$robust) {
        # variance L.var<-do.call('L.nvar', c(ingredients.list,
        # list(n.death=n.death, z.death=z.death,
        # eta.den.vec=eta.den.vec, eta=eta, dLambda1=dLambda1,
        # eta.cum=eta.cum, iA=object$iA, B=object$var,
        # time.pos=t.pos, bhaz=FALSE, newtime=newtime)))
        L.var <- do.call("L.nvar", c(ingr, object, list(t.pos = t.pos,
            pred = pred, newtime = newtime)))
    } else {
        L.resid <- do.call("cook.L.resid", c(ingr, list(time.pos = ingr$match.event)))
        # robust variance
        L.var <- do.call("L.rvar", c(ingr, object, list(t.pos = t.pos,
            pred = pred, newtime = newtime, L.resid = L.resid)))
    }
    L.se <- sqrt(L.var)
    return(data.frame(time = newtime, L = L, L.se = L.se))
}