Dear R-Epi users;i am trying to fit an illness-death model with recovery using both Epi (version 0.9.6) and coxph but my results are not similar. When fitting a time to death model and all other states censored I get equivalent results. I think I am doing everything right and can't really think of a mistake on my part unless I am missing something in Epi for multistate models.This is how I set up the Epi data:msm.lexis.dta <- Lexis(    entry=list(per=start),    exit=list(per=stop),    entry.status=from,     exit.status=to,     id=case,    data=msm2b)with(msm.lexis.dta, table(from,to))msm.lexis.glm <- splitLexis(msm.lexis.dta,    breaks=quantile(c(0,msm.lexis.dta$stop),probs=seq(0,1,by=1/10)),    time.scale="per")for(i in 1:2){    for(j in 1:3){        if(i != j){            cat("from ==",i,"to ==",j,"\n")            cat("Cox proportional hazards\n")            h.rate <- coxph(Surv(time=start,time2=stop,event=(to==j))~sex+age,data=subset(msm.lexis.dta,from==i))            print(round(ci.lin(h.rate, Exp=TRUE), 3));cat("\n");            cat("Poisson regression\n")            h.rate <- glm(as.numeric(to==j)~ns(per)+sex+age+offset(log(lex.dur)),                family=poisson,data=subset(msm.lexis.glm,from==i))            print(round(ci.lin(h.rate, Exp=TRUE), 3));cat("\n");        }    }}And the answers I get for my data are:from == 1 to == 2Cox proportional hazards          Estimate StdErr      z     P exp(Est.)  2.5% 97.5%sexFemale    0.084  0.141  0.594 0.553     1.088 0.824 1.435age         -0.001  0.008 -0.099 0.921     0.999 0.983 1.015Poisson regression            Estimate StdErr       z     P exp(Est.)  2.5% 97.5%(Intercept)   -0.176  0.154  -1.141 0.254     0.839 0.620 1.135ns(per)       -4.524  0.315 -14.379 0.000     0.011 0.006 0.020sexFemale     -0.085  0.079  -1.073 0.283     0.919 0.787 1.073age            0.003  0.004   0.626 0.531     1.003 0.994 1.012from == 1 to == 3Cox proportional hazards          Estimate StdErr      z     P exp(Est.)  2.5% 97.5%sexFemale    0.208  0.213  0.977 0.329     1.232 0.811 1.871age         -0.007  0.013 -0.553 0.580     0.993 0.968 1.018Poisson regression            Estimate StdErr      z     P exp(Est.)  2.5% 97.5%(Intercept)   -1.608  0.378 -4.252 0.000     0.200 0.095 0.420ns(per)       -5.893  0.855 -6.895 0.000     0.003 0.001 0.015sexFemale      0.265  0.182  1.456 0.145     1.304 0.912 1.863age           -0.008  0.011 -0.712 0.476     0.992 0.971 1.014Kind regards, Vumani
_________________________________________________________________


	[[alternative HTML version deleted]]

