R/coxsimSpline.R

Defines functions coxsimSpline

Documented in coxsimSpline

#' Simulate quantities of interest for penalized splines from Cox Proportional
#' Hazards models
#'
#' \code{coxsimSpline} simulates quantities of interest from penalized splines
#' using multivariate normal distributions.
#' @param obj a \code{\link{coxph}} class fitted model object with a penalized
#' spline. These can be plotted with \code{\link{simGG}}.
#' @param bspline a character string of the full \code{\link{pspline}} call
#' used in \code{obj}. It should be exactly the same as how you entered it in
#' \code{\link{coxph}}.
#' @param bdata a numeric vector of the splined variable's values.
#' @param qi quantity of interest to simulate. Values can be
#' \code{"Relative Hazard"}, \code{"First Difference"}, \code{"Hazard Ratio"},
#' and \code{"Hazard Rate"}. The default is \code{qi = "Relative Hazard"}.
#' Think carefully before using \code{qi = "Hazard Rate"}. You may be creating
#' very many simulated values which can be very computationally intensive to do.
#' Adjust the number of simulations per fitted value with \code{nsim}.
#' @param Xj numeric vector of fitted values for \code{b} to simulate for.
#' @param Xl numeric vector of values to compare \code{Xj} to. Note if
#' \code{qi = "Relative Hazard"} or \code{"Hazard Rate"} only \code{Xj} is
#' relevant.
#' @param nsim the number of simulations to run per value of \code{Xj}. Default
#' is \code{nsim = 1000}.
#' @param ci the proportion of simulations to keep. The default is
#' \code{ci = 0.95}, i.e. keep the middle 95 percent. If \code{spin = TRUE}
#' then \code{ci} is the confidence level of the shortest probability interval.
#' Any value from 0 through 1 may be used.
#' @param spin logical, whether or not to keep only the shortest probability
#' interval rather than the middle simulations. Currently not supported for
#' hazard rates.
#' @param extremesDrop logical whether or not to drop simulated quantity of
#' interest values that are \code{Inf}, \code{NA}, \code{NaN} and
#' \eqn{> 1000000} for \code{spin = FALSE} or \eqn{> 800} for \code{spin = TRUE}.
#' These values are difficult to plot \code{\link{simGG}} and may prevent
#' \code{spin} from finding the central interval.
#'
#' @return a \code{simspline} object
#'
#' @details Simulates relative hazards, first differences, hazard ratios, and
#' hazard rates for penalized splines from Cox Proportional Hazards models.
#' These can be plotted with \code{\link{simGG}}.
#' A Cox PH model with one penalized spline is given by:
#'  \deqn{h(t|\mathbf{X}_{i})=h_{0}(t)\mathrm{e}^{g(x)}}{h(t|X[i])=h[0](t)exp(g
#' (x))}
#'
#' where \eqn{g(x)} is the penalized spline function. For our post-estimation
#' purposes \eqn{g(x)} is basically a series of linearly combined coefficients
#' such that:
#'
#'  \deqn{g(x) = \beta_{k_{1}}(x)_{1+} + \beta_{k_{2}}(x)_{2+} + \beta_{k_{3}}(x)_{3+} + \ldots + \beta_{k_{n}}(x)_{n+}}{g(x) = \beta[k][1](x)[1+] + \beta[k][2](x)[2+] + \beta[k][3](x)[3+] + \ldots + \beta[k][n](x)[n+]}
#'
#' where \eqn{k} are the equally spaced spline knots with values inside of the
#' range of observed \eqn{x} and \eqn{n} is the number of knots.
#'
#' We can again draw values of each \eqn{\beta_{k_{1}},\: \ldots \beta_{k_{n}}}{\beta[k][1], \ldots \beta[k[n]}
#' from the multivariate normal distribution
#' described above. We then use these simulated coefficients to estimates
#' quantities of interest for a range covariate values. For example, the first
#' difference between two values \eqn{x_{j}}{x[j]} and \eqn{x_{l}}{x[l]} is:
#'
#'    \deqn{\%\triangle h_{i}(t) = (\mathrm{e}^{g(x_{j}) - g(x_{l})} - 1) * 100}
#' {FD(h[i](t)) = (exp(g(x[j]) - g(x[l])) - 1) * 100}
#'
#' Relative hazards and hazard ratios can be calculated by extension.
#'
#' Currently \code{coxsimSpline} does not support simulating hazard rates form
#' multiple stratified models.
#'
#' @examples
#' # Load Carpenter (2002) data
#' data("CarpenterFdaData")
#'
#' # Load survival package
#' library(survival)
#'
#' # Run basic model
#' # From Keele (2010) replication data
#' M1 <- coxph(Surv(acttime, censor) ~  prevgenx + lethal + deathrt1 +
#'            acutediz + hosp01  + pspline(hospdisc, df = 4) +
#'            pspline(hhosleng, df = 4) + mandiz01 + femdiz01 + peddiz01 +
#'            orphdum + natreg + vandavg3 + wpnoavg3 +
#'            pspline(condavg3, df = 4) + pspline(orderent, df = 4) +
#'            pspline(stafcder, df = 4), data = CarpenterFdaData)
#'
#' \dontrun{
#' # Simulate Relative Hazards for orderent
#' Sim1 <- coxsimSpline(M1, bspline = "pspline(stafcder, df = 4)",
#'                     bdata = CarpenterFdaData$stafcder,
#'                     qi = "Hazard Ratio",
#'                     Xj = seq(1100, 1700, by = 10),
#'                     Xl = seq(1099, 1699, by = 10), spin = TRUE)
#'
#' }
#' # Simulate Hazard Rates for orderent
#' Sim2 <- coxsimSpline(M1, bspline = "pspline(orderent, df = 4)",
#'                        bdata = CarpenterFdaData$orderent,
#'                        qi = "Hazard Rate",
#'                        Xj = seq(2, 53, by = 3), nsim = 100)
#'
#' @seealso \code{\link{simGG}}, \code{\link{survival}}, \code{\link{strata}},
#' and \code{\link{coxph}}
#'
#' @references Gandrud, Christopher. 2015. simPH: An R Package for Illustrating
#' Estimates from Cox Proportional Hazard Models Including for Interactive and
#' Nonlinear Effects. Journal of Statistical Software. 65(3)1-20.
#'
#' Luke Keele, "Replication data for: Proportionally Difficult:
#' Testing for Nonproportional Hazards In Cox Models", 2010,
#' \doi{10.7910/DVN/VJAHRG} V1 [Version].
#'
#' King, Gary, Michael Tomz, and Jason Wittenberg. 2000. ''Making the Most of
#' Statistical Analyses: Improving Interpretation and Presentation.'' American
#' Journal of Political Science 44(2): 347-61.
#'
#' Liu, Ying, Andrew Gelman, and Tian Zheng. 2013. ''Simulation-Efficient
#' Shortest Probability Intervals.'' Arvix.
#' \url{https://arxiv.org/pdf/1302.2142v1.pdf}.
#'
#' @import data.table
#' @importFrom stringr word str_match str_replace
#' @importFrom stats vcov
#' @importFrom survival basehaz
#' @importFrom MASS mvrnorm
#' @importFrom dplyr %>% rename
#' @export

coxsimSpline <- function(obj, bspline, bdata, qi = "Relative Hazard", Xj = 1,
                         Xl = 0, nsim = 1000, ci = 0.95, spin = FALSE,
                         extremesDrop = TRUE)
{
    strata <- QI <- X <- time <- NULL
    # Ensure that qi is valid
    qiOpts <- c("Relative Hazard", "First Difference", "Hazard Rate",
                "Hazard Ratio")
    TestqiOpts <- qi %in% qiOpts
    if (!isTRUE(TestqiOpts)){
        stop("Invalid qi type. qi must be 'Relative Hazard', 'Hazard Rate', 'First Difference', or 'Hazard Ratio'.",
            call. = FALSE)
    }

    if (nsim > 10 & qi == "Hazard Rate"){
        message(paste0("Warning: finding Hazard Rates for spline models with ",
            nsim, " simulations per fitted value may take awhile.  Consider decreasing the number of simulations with nsim."))
    }

    if (is.null(Xl) & qi != "Hazard Rate"){
        Xl <- rep(0, length(Xj))
        message("All Xl set to 0.")
    } else if (!is.null(Xl) & qi == "Relative Hazard") {
        Xl <- rep(0, length(Xj))
        message("All Xl set to 0.")
    }

    # Standardise pspline term with white space around '='
    Before <- grepl(pattern = "[^ ]=", x = bspline)
    if (isTRUE(Before)) bspline <- gsub(pattern = "=", replacement = ' =',
                                       x = bspline)
    bspline <- gsub(pattern = "[^ ]=", replacement = ' =', x = bspline)
    After <- grepl(pattern = "=[^ ]", x = bspline)
    if (isTRUE(After)) bspline <- gsub(pattern = "=", replacement = '= ',
                                       x = bspline)

    # Find term number
    TermNum <- names(obj$pterms)
    bterm <- match(bspline, TermNum)
    if (is.na(bterm)){
        stop(paste0("Unable to find ", bspline, "."), call. = FALSE)
    }

    # Extract boundary knots for default Boundary.knots = range(x) &
    # number of knots
    #### Note: these can also be found with
    # get("cbase", environment(obj$printfun[[1]]))
    # (replace 1 with the spline term number)
    #### From:
    # https://r.789695.n4.nabble.com/help-on-pspline-in-coxph-td3431829.html
    OA <- obj$assign
    ListKnots <- OA[bterm]
    NumKnots <- length(unlist(ListKnots))
    KnotIntervals <- levels(cut(bdata, breaks = NumKnots))

    # Create simulation ID variable
    SimID <- 1:nsim

    # Parameter estimates & Variance/Covariance matrix
    Coef <- matrix(obj$coefficients)
    VC <- vcov(obj)

    # Draw covariate estimates from the multivariate normal distribution
    Drawn <- mvrnorm(n = nsim, mu = Coef, Sigma = VC)
    DrawnDF <- data.frame(Drawn)
    dfn <- names(DrawnDF)

    # Subset data frame to only spline variable coefficients.
    bword <- word(bspline, 1)
    b <- str_replace(bword, "pspline\\(", "")
    b <- str_replace(b, ",", "")

    NamesLoc <- function(p){
        Temp <- paste0("ps.", b, ".", p)
        match(Temp, dfn)
    }

    UpLim <- 2 + NumKnots
    CoeNum <- as.numeric(3:UpLim)
    NameCoe <- sapply(CoeNum, NamesLoc, simplify = TRUE)
    DrawnDF <- data.frame(DrawnDF[, NameCoe])

    # Match coefficients to knot interval
    IntervalStartAbs <- "\\(-?[0-9]*.[0-9]*e?\\+?[0-9]*,"
    IntervalFinishAbs <- ",-?[0-9]*.[0-9]*e?\\+?[0-9]*\\]"
    IntervalStart <- str_match(KnotIntervals, IntervalStartAbs)
    IntervalStart <- str_replace(IntervalStart, "\\(", "")
    IntervalStart <- str_replace(IntervalStart, ",", "")
    IntervalStart <- as.numeric(IntervalStart)

    IntervalFinish <- str_match(KnotIntervals, IntervalFinishAbs)
    IntervalFinish <- str_replace(IntervalFinish, "\\]", "")
    IntervalFinish <- str_replace(IntervalFinish, ",", "")
    IntervalFinish <- as.numeric(IntervalFinish)

    CoefIntervals <- data.frame(names(DrawnDF), IntervalStart, IntervalFinish)
    names(CoefIntervals) <- c("CoefName", "IntervalStart", "IntervalFinish")

    # Melt Drawn DF to long format
    DrawnDF <- data.frame(SimID, DrawnDF) %>% as.data.table
    TempDF <- suppressMessages(data.frame(melt(DrawnDF,
                               id.vars = 'SimID')))
    names(TempDF) <- c("SimID", "CoefName", "Coef")

    # Merge with CoefIntervals
    CoefIntervalsDT <- data.table(CoefIntervals, key = "CoefName")
    TempDT <- data.table(TempDF, key = "CoefName")
    TempCombDT <- TempDT[CoefIntervalsDT]
    TempDF <- data.frame(TempCombDT)

    # Merge in fitted X values
    MergeX <- function(f){
        X <- NULL
        CombinedDF <- data.frame()
        for (i in f){
          Temps <- TempDF
          Temps$X <- ifelse(TempDF[, 'IntervalStart'] < i & i <=
                              TempDF[, 'IntervalFinish'], i, NA)
          Temps <- subset(Temps, !is.na(X))
          CombinedDF <- rbind(CombinedDF, Temps)
        }
        CombinedDF
    }

    # Find quantities of interest
    if (qi == "Hazard Ratio"){
          if (length(Xj) != length(Xl)){
          stop("Xj and Xl must be the same length.", call. = FALSE)
        }
        Simbj <- MergeX(Xj) %>% rename(Xj = X)
        Simbl <- MergeX(Xl) %>% rename(Xl = X)
        Xs <- data.frame(Xj, Xl)
        Simbj <- merge(Simbj, Xs, by = "Xj")
        Simbj$Comparison <- paste(Simbj$Xj, "vs.", Simbj$Xl)
        Simbj$QI <- exp((Simbj$Xj * Simbj$Coef) - (Simbl$Xl * Simbl$Coef))
        Simb <- Simbj
    } else if (qi == "Relative Hazard"){
        Simb <- MergeX(Xj) %>% rename(Xj = X)
        Simb$QI <- exp(Simb$Xj * Simb$Coef)
    }
    else if (qi == "First Difference"){
            if (length(Xj) != length(Xl)){
            stop("Xj and Xl must be the same length.", call. = FALSE)
        }
        else {
            Simbj <- MergeX(Xj) %>% rename(Xj = X)
            Simbl <- MergeX(Xl) %>% rename(Xl = X)

            Xs <- data.frame(Xj, Xl)
            Simbj <- merge(Simbj, Xs, by = "Xj")
            Simbj$Comparison <- paste(Simbj$Xj, "vs.", Simbj$Xl)

            Simbj$QI <- (exp((Simbj$Xj * Simbj$Coef) -
                         (Simbl$Xl * Simbl$Coef)) - 1) * 100
            Simb <- Simbj
        }
    }
    else if (qi == "Hazard Rate"){
        Xl <- NULL
        message("Xl is ignored. All variables' values other than b fitted at 0.")
        Simb <- MergeX(Xj) %>% rename(Xj = X)
        Simb$HR <- exp(Simb$Xj * Simb$Coef)
        bfit <- basehaz(obj)
        ## Currently does not support strata
        if ('strata' %in% names(obj)){
            stop("coxsimSpline currently does not support strata.",
                call. = FALSE)
        }
        bfit$FakeID <- 1
        Simb$FakeID <- 1
        bfitDT <- data.table(bfit, key = "FakeID", allow.cartesian = TRUE)
        SimbDT <- data.table(Simb, key = "FakeID", allow.cartesian = TRUE)
        Simb <- SimbDT[bfitDT, allow.cartesian = TRUE]

        # Create warning message
        Rows <- nrow(Simb)
        if (Rows > 2000000){
        message(paste("There are", Rows,
            "simulations. This may take awhile. Consider using nsim to reduce the number of simulations."))
        }
        Simb$QI <- Simb$hazard * Simb$HR
        if (!('strata' %in% names(Simb))){
            Simb <- Simb[, list(time, SimID, Xj, QI)]
        } else if ('strata' %in% names(Simb)){
            Simb <- Simb[, list(time, SimID, Xj, QI, strata)]
        }
        Simb <- data.frame(Simb)
    }

    # Drop simulations outside of 'confidence bounds'
    if (qi != "Hazard Rate"){
        SubVar <- "Xj"
    } else if (qi == "Hazard Rate"){
        SubVar <- c("time", "Xj")
    }

    # Drop simulations outside of the middle
    SimbPerc <- IntervalConstrict(Simb = Simb, SubVar = SubVar,
                                  qi = qi, spin = spin, ci = ci,
                                  extremesDrop = extremesDrop)

    # Final clean up
    # Subset simspline object & create a data frame of important variables
    if (qi == "Hazard Rate"){
        if (!('strata' %in% names(SimbPerc))){
            SimbPercSub <- data.frame(SimbPerc$SimID, SimbPerc$time,
                SimbPerc$QI, SimbPerc$Xj)
            names(SimbPercSub) <- c("SimID", "Time", "QI", "Xj")
        }
        # Currently does not support strata
        else if ('strata' %in% names(SimbPerc)) {
            stop("coxsimSpline currently doesn't support Hazard Rates when there are multiple stata. Sorry.",
                call. = FALSE)
        }
    } else if (qi == "Hazard Ratio"){
        SimbPercSub <- data.frame(SimbPerc$SimID, SimbPerc$Xj, SimbPerc$QI,
                            SimbPerc$Comparison)
        names(SimbPercSub) <- c("SimID", "Xj", "QI", "Comparison")
    } else if (qi == "Relative Hazard"){
        SimbPercSub <- data.frame(SimbPerc$SimID, SimbPerc$Xj, SimbPerc$QI)
        names(SimbPercSub) <- c("SimID", "Xj", "QI")
    } else if (qi == "First Difference"){
        SimbPercSub <- data.frame(SimbPerc$SimID, SimbPerc$Xj, SimbPerc$QI,
            SimbPerc$Comparison)
        names(SimbPercSub) <- c("SimID", "Xj", "QI", "Comparison")
    }
    # Add in distribution of b
    b <- gsub('^pspline\\(', '', bspline)
    b <- gsub(',.*)$', '', b)
    rug <- bdata
    out <- list(sims = SimbPercSub, rug = rug)

    class(out) <- c("simspline", qi, "coxsim")
    attr(out, 'xaxis') <- b
    out
}

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simPH documentation built on Jan. 13, 2021, 6:52 a.m.