In this example I illustrate my point my simulate data from exponential distributions. Using the similary of likelihood functions I can estimate the rate by Poisson regression.  Note that I dont fit random observations as what I have on left side of the model is the number of observations, Therefore it is meaningless to talk about Poisson distributed observations and so it will not make sense to verify if data is Poisson distributed.   data silly_data; do group=1 to 2; do i=1 to 1000; event=1; time=rand('exponential',4); logtime=log(time); output; end; end; run; proc summary data=silly_data nway; var time; class group; output out=summary sum=sumtime; ruN; data summary; set summary; logtime=log(sumtime); run; *estimate the rate paramater using aggregated form of the data; proc genmod data=summary; model _freq_=/dist=poisson link=log dist=poisson offset=logtime; estimate 'rate' intercept 1; run; *same estimate can be obtained by using unaggregated form of the data; proc genmod data=silly_data; model event=/dist=poisson link=log dist=poisson offset=logtime; estimate 'rate' intercept 1; run; ... View more

I will also vote not, but for other reason that what @SteveDenham mention.   You can completely ignore overdispersion in such Poisson regression model. The reason is that the data doesn't need to be Poisson distributed. Actually, the data which is behind your number of events is time-to-event data. If the assumption of piecewise constant rates are fullfilled, then data can be analyzed by poisson regression because the likelihood function in the Poisson regression is exactly the likelihood function you want to maximize if you had the original time-to-event data. Therefore, it is wrong to use Poisson regression in such model to validate the distribution of data, it is only a trick to maximize the likelihood function and thereby make estimates and relevant hyphotesis testing about covariates.   It is actually quite easy to verify: simulate n datapoints from exponential distribution then cumulate the values. you can now estimate the rate using poisson regression (model n=/dist=poisson link=log offset=logcumtime). In such model it is obvious that it is meaning less to talk about overdispersion even that the dispersion index will be showed. So just forget about dispersion in Poisson regression.   If the data was truly count-data, then it is much more relevant to look on the assumption of poisondistributed data, and then the dispersion index is much more relevant. ... View more

its because the expression for the likelihood function λ x e -pyrs*λ is the same as (λ x/pyrs e -λ ) pyrs ... View more

I think that you can model the rate and get exactly same estimates and standard errors as if you model the count. But, the person-years should then be used in the weight statement.   The two proc genmod statements below seems to give exactly same output. data test; do i=1 to 100; pyrs=rand('uniform',0,5); a=rand('bernoulli',0.5); y=rand('poisson',pyrs*exp(2+a*1)); logpyrs=log(pyrs); rate=y/pyrs; output; end; run; proc genmod data=test; class a(ref="0"); model y=a/dist=poisson link=log offset=logpyrs; run; proc genmod data=test; class a(ref="0"); model rate=a/dist=poisson link=log; weight pyrs; run;   ... View more

Yes, that wil work also. If those with dummytime=2 is the same as those with Y=0, then model dummytime*Y(0)/ties=discrete is equivalent to model dummytime*dummytime(2)/ties=discrete   given of course that the rest has dummytime=1. ... View more

It is true that proc logistic can not make the Breslow or Efron approximation. But, construction af variable "dummytime" with values 1 for cases and 0 for controls, and analyze this as survival analysis is equivalent to conditional logistic regression when using the option ties=discrete in PROC PHREG. If, instead, the option ties=breslow (default) or ties=efron is used, then you have the approximation. ... View more

Thank you, it was something like that I needed when I updated to SAS 9.4M4 a few days ago. ... View more