PrenticeGloeckler: Regression for Grouped Survival Data Function

In SurvDisc: Discrete Time Survival and Longitudinal Data Analysis

Description

This function calculates the estimated hazard ratio for grouped survival data described in the reference below.

Usage

PrenticeGloeckler.test(time,event,grp,r)

Arguments

time	vector of times to event or censoring. The times are assumed to be integers from 1, 2, .., r corresponding to the discrete time points or the continuous time intervals A1, ..., Ar
event	vector of binary status indicator variables (0 = censored at the start of the interval, 1 = event during the interval)
grp	vector of binary group indicators (0 or 1)
r	number of time points or intervals

Details

The hazard functions and hazard ratio are estimated for grouped survival data.

Value

A list consisting of:

coefficient	The estimated coefficient (log hazard ratio) found by maximizing the likelihood.
indx	vector of time points where the hazard functions are estimated. The subset of 1,...,r-1 with at least one event.
gamma	numeric vector with the same length as indx representing the log(-log(hazard rate)) in the control group for time points in the vector indx
grad1	gradient evaluated at (gamma[indx],ceofficient)
r	number of time points or time intervals
hess1	hessian matrix evaluated at the maximum likelihood estimate.
ll0	log-likelihood evaluated at ceofficient=0. includes attributes "gradient" and "hessian"
ll1	log-likelihood at maximum likeohood estimate. includes attributes "gradient" and "hessian"
score.test	value of the score test statistic for testing coefficient=0 (see reference).
lr.test	value of the likelihood ratio test statistic, 2*(ll0-ll1)
wald.test	value of the Wald test statistic; the estimated coefficient divided by the square root of the estimated variance.

Author(s)

John Lawrence

References

Prentice, R. L. and Gloeckler, L.A. (1978). Regression analysis of grouped survival data with application to breast cancer data. Biometrics, 57 – 67

Examples

set.seed(1234)
nsim=1
n=250
tn=2*n
k=0.1*tn
betaef=rep(0,nsim)
betapg=rep(0,nsim)
cens=rep(1,2*n)
trt=c(rep(0,n),rep(1,n))

for (i in 1:nsim) {
  x=rexp(tn,1)
  x[(n+1):tn]=x[(n+1):tn]/2
  m1=max(x[(n+1):tn])
  x=ceiling(x*(k-1)/m1)
  x[(n+1):tn]=pmin(x[(n+1):tn],k-1)
  x[1:n]=pmin(x[1:n],k)
  pg1=PrenticeGloeckler.test(x,cens,trt,k)
  betapg[i]=pg1$coefficient
  betaef[i]=survival::coxph(survival::Surv(x,cens)~trt,ties="efron")$coef}
mean(betaef)
mean(betapg)

Example output

[1] 0.70625
[1] 0.709874

SurvDisc documentation built on May 2, 2019, 9:12 a.m.