phmm-package: Proportional Hazards with Mixed Model (PHMM)

In phmm: Proportional Hazards Mixed-Effects Model

Description

Fits proportional hazards model incorporating random effects. The function implements an EM agorithm using Markov Chain Monte Carlo at the E-step as described in Vaida and Xu (2000).

Details

Package:	phmm
Version:	0.2
Date:	2008-01-15
Depends:	survival
Suggests:	lme4
License:	GPL2
Packaged:	Fri Jul 11 10:33:57 2008; mdonohue
Built:	R 2.8.0; universal-apple-darwin8.11.1; 2008-11-29 12:05:00; unix

Index:

 AIC.phmm Akaike Information Criterion for PHMM cAIC
Conditional Akaike Information Criterion for PHMM e1582 Eastern Cooperative
Oncology Group (EST 1582) linear.predictors PHMM Design loglik.cond PHMM
conditional log-likelihood phmm Proportional Hazards Model with Mixed
Effects phmm-package Proportional Hazards Model with Mixed Effects
phmm.cond.loglik PHMM conditional log-likelihood phmm.design PHMM Design
pseudoPoisPHMM Pseudo poisson data for fitting PHMM via GLMM traceHat Trace
of the "hat" matrix from PHMM-MCEM fit 

Author(s)

Ronghui Xu, Michael Donohue

Maintainer: Michael Donohue [e-mail]

References

Vaida, F. and Xu, R. "Proportional hazards model with random effects", Statistics in Medicine, 19:3309-3324, 2000.

Donohue, MC, Overholser, R, Xu, R, and Vaida, F (January 01, 2011). Conditional Akaike information under generalized linear and proportional hazards mixed models. Biometrika, 98, 3, 685-700.

Examples

n <- 50      # total sample size
nclust <- 5  # number of clusters
clusters <- rep(1:nclust,each=n/nclust)
beta0 <- c(1,2)
set.seed(13)
#generate phmm data set
Z <- cbind(Z1=sample(0:1,n,replace=TRUE),
           Z2=sample(0:1,n,replace=TRUE),
           Z3=sample(0:1,n,replace=TRUE))
b <- cbind(rep(rnorm(nclust),each=n/nclust),rep(rnorm(nclust),each=n/nclust))
Wb <- matrix(0,n,2)
for( j in 1:2) Wb[,j] <- Z[,j]*b[,j]
Wb <- apply(Wb,1,sum)
T <- -log(runif(n,0,1))*exp(-Z[,c('Z1','Z2')]%*%beta0-Wb)
C <- runif(n,0,1)
time <- ifelse(T<C,T,C)
event <- ifelse(T<=C,1,0)
mean(event)
phmmd <- data.frame(Z)
phmmd$cluster <- clusters
phmmd$time <- time
phmmd$event <- event

fit.phmm <- phmm(Surv(time, event) ~ Z1 + Z2 + (-1 + Z1 + Z2 | cluster), 
   phmmd, Gbs = 100, Gbsvar = 1000, VARSTART = 1,
   NINIT = 10, MAXSTEP = 100, CONVERG=90)
summary(fit.phmm)

phmm documentation built on March 26, 2020, 5:10 p.m.