coxph.detail: Details of a Cox Model Fit

Description

Returns the individual contributions to the first and second derivative matrix, at each unique event time.

Usage

coxph.detail(object, riskmat=FALSE)

Arguments

object

a Cox model object, i.e., the result of coxph.

riskmat

include the at-risk indicator matrix in the output?

Value

a list with components
timethe vector of unique event times
neventthe number of events at each of these time points.
meansa matrix with one row for each event time and one column for each variable in the Cox model, containing the weighted mean of the variable at that time, over all subjects still at risk at that time. The weights are the risk weights exp(x %*% fit$coef).
nrisknumber of subjects at risk.
scorethe contribution to the score vector (first derivative of the log partial likelihood) at each time point.
imatthe contribution to the information matrix (second derivative of the log partial likelihood) at each time point.
hazardthe hazard increment. Note that the hazard and variance of the hazard are always for some particular future subject. This routine uses object$mean as the future subject.
varhazthe variance of the hazard increment.
x,ycopies of the input data.
strataonly present for a stratified Cox model, this is a table giving the number of time points of component time that were contributed by each of the strata.
riskmata matrix with one row for each time and one column for each observation containing a 0/1 value to indicate whether that observation was (1) or was not (0) at risk at the given time point.

Details

This function may be useful for those who wish to investigate new methods or extensions to the Cox model. The example below shows one way to calculate the Schoenfeld residuals.

Examples

Run this code

fit   <- coxph(Surv(futime,fustat) ~ age + rx + ecog.ps, ovarian, x=TRUE)
fitd  <- coxph.detail(fit)
#  There is one Schoenfeld residual for each unique death.  It is a
# vector (covariates for the subject who died) - (weighted mean covariate
# vector at that time).  The weighted mean is defined over the subjects
# still at risk, with exp(X beta) as the weight.

events <- fit$y[,2]==1
etime  <- fit$y[events,1]   #the event times --- may have duplicates
indx   <- match(etime, fitd$time)
schoen <- fit$x[events,] - fitd$means[indx,]