pwexpfit: Piecewise exponential survival estimation

In keaven/nphsim: Non proportional hazards sample size and simulation

Description

Computes survival function, density function, -2*log-likelihood based on input dataset and intervals for piecewise constant failure rates. Initial version assumes observations are right censored or events only.

Usage

pwexpfit(Srv = Surv(time = Ex1delayedEffect$month, event =
  Ex1delayedEffect$evntd), intervals = array(3, 3))

Arguments

Srv	input survival object (see Surv; note that only 0=censored, 1=event for Surv
intervals	Vector containing positive values indicating interval lengths where the exponential rates are assumed. Note that a final infinite interval is added if any events occur after the final interval specified.

Value

A matrix with rows containing interval length, estimated rate, -2*log-likelihood for each interval.

Examples

# use default arguments for delayed effect example dataset (Ex1delayedEffect)
rateall <- pwexpfit()
rateall
# Estimate by treatment effect
rate1 <- with(subset(Ex1delayedEffect,trt==1), pwexpfit(Surv(month,evntd)))
rate0 <- with(subset(Ex1delayedEffect,trt==0), pwexpfit(Surv(month,evntd)))
rate1
rate0
rate1$rate/rate0$rate
# chi-square test for (any) treatment effect (8 - 4 parameters = 4 df)
pchisq(sum(rateall$m2ll)-sum(rate1$m2ll+rate0$m2ll), df = 4, lower=F)
# compare with logrank
survdiff(formula = Surv(month, evntd) ~ trt)
# simple model with 3 rates same for each for 3 months, 
# different for each treatment after months
rate1a <- with(subset(Ex1delayedEffect,trt==1), pwexpfit(Surv(month,evntd),3))
rate0a <- with(subset(Ex1delayedEffect,trt==0), pwexpfit(Surv(month,evntd),3))
rate1a$rate/rate0a$rate
m2ll0 <- rateall$m2ll[1]+rate1a$m2ll[2]+rate0a$m2ll[2]
m2ll1 <- sum(rate0$m2ll)+sum(rate1$m2ll)
# as a measure of strength, chi-square examines improvement in likelihood
pchisq(m2ll0-m2ll1,5,lower=FALSE)

keaven/nphsim documentation built on May 24, 2020, 9:34 p.m.