Dear R-users,
I need to use the aftreg function in package 'eha' to estimate failure times
for left truncated survival data. Apparently, survreg still cannot fit such
models. Both functions should be fitting the accelerated failure time (Weibull)
model. However, as Göran Broström points out in the help file for aftreg, the
parameterisation is different giving rise to different coefficients. The betas
for adjusted covariates are opposite in sign but otherwise identical, whereas
the intercept is quite different in a non-obvious way. The log-likelihoods are
similar also, but not identical. I would like to find out how I can convert one
set of coefficients to the other so as to obtain the same linear predictors
using either model. Any ideas???
#the example below uses right-censored data for simplicity (the principle
should be the same with left truncation I hope)
library(survival)
library(eha)
# COMPARE coefs between survreg ('survival' pkg) and aftreg ('eha' pkg)
#Fitting NULL models (no covariates) results in (approximately) the same coefs
(which is good!)
m1_NULL=survreg(Surv(futime/365, status==1) ~ 1, data=pbcseq)
m2_NULL=aftreg(Surv(futime/365, status==1) ~ 1, data=pbcseq)
c(m1_NULL$coef, 1/m1_NULL$scale) #--> intercept= 3.878656 , shape = 1.478177
c(m2_NULL$coef[1], exp(m2_NULL$coef[2])) #--> intercept= 3.878859 ,
shape=1.478150
# NOW I adjust for covariates
m1=survreg(Surv(futime/365, status==1) ~ chol+stage, data=pbcseq)
m2= aftreg(Surv(futime/365, status==1) ~ chol+stage, data=pbcseq)
### m2 #######
#Coefficients:
# (Intercept) chol stage
# 5.944641913 -0.001692574 -0.470861324
#Scale= 0.6416744
#Loglik(model)= -483.9 Loglik(intercept only)= -506.8
# Chisq= 45.91 on 2 degrees of freedom, p= 1.1e-10
#n=1124 (821 observations deleted due to missingness)
### m2 #######
#Covariate W.mean Coef Exp(Coef) se(Coef) Wald p
#chol 303.777 0.002 1.002 0.000 0.000
#stage 3.298 0.460 1.584 0.119 0.000
#
#log(scale) 5.029 152.807 0.477 0.000
#log(shape) 0.467 1.595 0.095 0.000
#
#Events 92
#Total time at risk 9017
#Max. log. likelihood -484.31
#LR test statistic 45.0
#Degrees of freedom 2
#Overall p-value 1.64669e-10
Many thanks for any help you may be able to provide.
Eleni Rapsomaniki
Research Associate
University of Cambridge
Institute of Primary and Public Health
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