I have a cohort of patients with different length of follow-up. So far I´m disregarding the time aspect and just need to model a binary outcome-disease/no disease. I usually do logistic regression in these studies, but another collegue of mine asked if Poisson regression would be just as appropriate. I´m not that into poisson and was left uncertain as to what the benefits and disadvantages of doing poisson in this setting would be compared logistic regression. I read Poisson regression to estimate relative risk for binary outcomes and I am still uncertain as to the merits of poisson regression in this situation.


2 Answers 2


One solution to this problem is to assume that the number of events (like flare-ups) is proportional to time. If you denote the individual level of exposure (length of follow-up in your case) by $t$, then $\frac{E[y \vert x]}{t}=\exp\{x'\beta\}.$ Here a follow-up that is twice as long would double the expected count, all else equal. This can be algebraically equivalent to a model where $E[y \vert x]=\exp\{x'\beta+\log{t}\},$ which is just the Poisson model with the coefficient on $\log t$ constrained to $1$. You can also test the proportionality assumption by relaxing the constraint and testing the hypothesis that $\beta_{log(t)}=1$.

However, it does not sound like you observe the number of events, since your outcome is binary (or maybe it's not meaningful given your disease). This leads me to believe a logistic model with an logarithmic offset would be more appropriate here.


This dataset sounds like a person-years dataset, the outcome being an event (is this correct?) and uneven follow-up until the event. In that case, this is sounds like a cohort study of some sort (assuming I understood what is being researched), and thus, either poisson regression OR a survival analysis may be warranted (kaplan-meier & cox-proportional hazards regression).

  • $\begingroup$ Wouldn't the response be more like binomial than Poisson? $\endgroup$ Apr 23, 2018 at 6:08
  • $\begingroup$ True, but a 0/1 response (binomial) dataset can be turned into a count dataset. Effectively, you collapse into groups/strata by predictors, then sum the number of events and separately the number of person years. Time-to-event (survival data) can be analysed as survival or as count data, the simpler option often is the survival analysis. $\endgroup$
    – Nicolas
    Apr 24, 2018 at 6:27
  • $\begingroup$ Isn't that like turning a 0/1 response (Bernouilli) dataset, into a count dataset. You only end up with a Poisson distribution/process by approximation of the Binomial distribution (for the finite cohort size). $\endgroup$ Apr 24, 2018 at 8:06
  • $\begingroup$ @NicolasSmoll "True, but a 0/1 response (binomial) dataset can be turned into a count dataset." How to do that? $\endgroup$
    – vasili111
    Jul 9, 2019 at 14:45

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.