This is a question about time-varying effects rather than time-varying covariates, even if the SAS method tests for the former by using the latter. SAS evaluates the line
>> dosetime=time*dose; for all observations at each event time as it estimates the model, such that you are not using future information. It has the effect of testing for a linear change in the magnitude of the effect of dose over time. I believe Paul Allison's survival book recommends this as a quick and dirty test for constancy of effect. Had you put that line in a datastep prior to PHREG, rather than in PHREG, you'd get a completely different (and uninformative) result (probably the same as R is giving you), because each observation's total survival time would be used to create a single value for the interaction term. You could manually replicate SAS's behaviour in R if you wanted, but every observation would have to start a new time interval whenever any other observation has an event, as Peter explained below. You might also want to look at Aalen's additive survival model for non-linear changes in effect over time: http://www.med.uio.no/imb/stat/addreg/ hope that helps, Jacob Etches On 2005/06/22, at 06:34, Peter Dalgaard wrote: > "Marianne dk" <[EMAIL PROTECTED]> writes: > >> I have a dataset with >> >> event=death >> time (from medical examination until death/censoring) >> dose (given at examination time) >> >> Two groups are considered, a non-exposed group (dose=0), an exposed >> group >> (dose between 5 and 60). >> >> For some reason there is a theory of the dose increasing its effect >> over >> time (however it was only given (and measured) once = at the time of >> examination). >> >> I tested a model: >> >> coxph(Surv(time,dod)~dose + dose:time) >> >> Previously I tested the model in SAS: >> >> proc phreg data=test; >> model time*dod(0)=dose dosetime /rl ties=efron; >> dosetime=time*dose; >> run; >> >> Without the interaction terms I get the same results for the two >> models. By >> including the interaction terms I do not. The model in R gives a >> negative >> coefficient for the interaction term which is expected to be positive >> (and >> is so in SAS). The LRTs are also completely different. >> >> TWO QUESTIONS: >> >> 1) Is it reasonable to bring in an interaction term when dose is only >> measured once? >> >> 2) If yes, can anyone give a hint on explaining the difference >> between the >> models in R and SAS? > > I don't know what SAS does, maybe it second-guesses your intentions, > but R will definitely get it completely wrong. If you use time as a > covariate, the same time (of death/censoring) will be applied at all > death times. Pretty obviously, long observation times tend to be > associated with low mortalities! With interactions you get, er, > similarly incorrect effects. > > To do coxph with time-dependent variables, you need to split data > into little time segments, according to the death time of every death, > inserting a new variable (ntime, say) which is the time of the > endpoint of the interval. > > -- > O__ ---- Peter Dalgaard Ă˜ster Farimagsgade 5, Entr.B > c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K > (*) \(*) -- University of Copenhagen Denmark Ph: (+45) > 35327918 > ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) > 35327907 > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html