Why don't discrete time survival models require a random effect? I learned that you can use random effects as a way to induce correlation in regression models. Say, we measure cholesterol over a 12 week period. The random effect "tells" the regression model that the data isn't independent: person's response in week 1 should be correlated to their response in week 2 and so on. Of course, you can specify more complicated types of correlation (I believe a simple random effect induces a compound symmetry correlation structure).
When you fit a discrete time survival model, you first transform the time-to-event data into counting process format (sometimes called person-period data) where rows are pseudo-observations for a person in each time period. Then you fit a model for binary data, usually logistic regression, to all the pseudo-observations.
I don't see people adding random effects or specifying a correlation structure in discrete time survival models. What "tells" the model that the multiple rows (pseudo-observations) for each person are not independent? Isn't the model treating pseudo observations as uncorrelated when they should have some correlation?


I don't see people adding random effects or specifying a correlation structure in discrete time survival models.

Tutz & Schmid "Modeling Discrete Time-to-Event Data" (2016) Chapter 9 "Frailty Models and Heterogeneity" covers both random and fixed effects models of the exact type you are describing. The motivation for random or fixed effects given there is about the same as you describe. Thus it is in conflict with your title question; discrete time survival models may well require a random effect.
Why have you not seen such models before? Perhaps since they may be difficult to estimate? (Just a guess.) At least I have had significant trouble with that; see "Simulating and estimating generalized mixed effects models: how to avoid convergence problems?".
