hds (version 0.8.1)

hds: Hazard discrimination summary estimator

Description

Returns hazard discimination summary (HDS) estimates at all specified evaluation times. See Liang and Heagerty (2016) for details on HDS.

Usage

hds(times, status, m, evaltimes = times[order(times)], se = TRUE)

Arguments

times

A vector of observed follow up times.

status

A vector of status indicators, usually 0=alive, 1=dead.

m

A matrix or data frame of covariate values, with a column for each covariate and each observation is on a separate row. Non-numeric values are acceptable, as the values will be transformed into a numeric model matrix through survival::coxph.

evaltimes

A vector of times at which to estimate HDS. Defaults to all the times specified by the times vector. If there are a lot of observations, then you may want to enter in a sparser vector of times for faster computation.

se

TRUE or FALSE. TRUE: calculate and return standard error estimates. FALSE: do not calculate standard errors estimates and return NAs. Defaults to TRUE. May want to set to FALSE to save computation time if using this function to compute bootstrap standard errors.

Value

A data frame with three columns: 1) the evaluation times, 2) the HDS estimates at each evaluation time, and 3) the standard error estimates at each evaluation time

Details

A wrapper for hds_t. Since hds_t only estimates HDS at one time point, this function calls hds_t multiple times to estimate the entire HDS curve. hds and hdslc are the main functions the user will interact with in this package.

The covariate values m are centered for numerical stability. This is particularly relevant for the standard error calculations.

References

Liang CJ and Heagerty PJ (2016). A risk-based measure of time-varying prognostic discrimination for survival models. Biometrics. doi:10.1111/biom.12628

See Also

hdslc

Examples

## Not run: 
# head(hds(times = survival::pbc[1:312, 2],
#          status = (survival::pbc[1:312, 3]==2)*1,
#          m = survival::pbc[1:312, 5]))
# 
# hdsres   <- hds(times=pbc5[,1], status=pbc5[,2], m=pbc5[,3:7])
# hdslcres <- hdslc(times = pbc5[,1], status=pbc5[,2], m = pbc5[,3:7], h = 730)
# Survt    <- summary(survival::survfit(survival::Surv(pbc5[,1], pbc5[,2])~1))
# Survtd   <- cbind(Survt$time, c(0,diff(1-Survt$surv)))
# tden     <- density(x=Survtd[,1], weights=Survtd[,2], bw=100, kernel="epanechnikov")
# 
# par(mar=c(2.25,2.25,0,0)+0.1, mgp=c(1.25,0.5,0))
# plot(c(hdslcres[,1], hdslcres[,1]), c(hdslcres[,2] - 1.96*hdslcres[,3],
#                                       hdslcres[,2] + 1.96*hdslcres[,3]),
#      type="n", xlab="days", ylab="HDS(t)", cex.lab=.75, cex.axis=.75,
#      ylim=c(-3,15), xlim=c(0,3650))
# polygon(x=c(hdsres[,1], hdsres[312:1,1]), col=rgb(1,0,0,.25), border=NA,
#         fillOddEven=TRUE, y=c(hdsres[,2]+1.96*hdsres[,3],
#                               (hdsres[,2]-1.96*hdsres[,3])[312:1]))
# polygon(x=c(hdslcres[,1], hdslcres[312:1, 1]), col=rgb(0,0,1,.25), border=NA,
#         fillOddEven=TRUE, y=c(hdslcres[,2] + 1.96*hdslcres[,3],
#                               (hdslcres[,2] - 1.96*hdslcres[,3])[312:1]))
# lines(hdsres[,1], hdsres[,2], lwd=2, col="red")
# lines(hdslcres[,1], hdslcres[,2], lwd=2, col="blue")
# abline(h=1, lty=3)
# legend(x=1200, y=14, legend=c("Proportional hazards",
#                               "Local-in-time proportional hazards",
#                               "Time density"), col=c("red", "blue", "gray"),
#        lwd=2, bty="n", cex=0.75)
# with(tden, polygon(c(x, x[length(x):1]),
#                    c(y*3/max(y)-3.5, rep(-3.5, length(x))),
#                    col="gray", border=NA, fillOddEven=TRUE))
# ## End(Not run)