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dear allstatters i have a problem with survival data that are interval censored at both ends. i am working on a large observational database, where for each patient i know the value of a boolean variable X at various times T. The starting value at baseline is 'True', so the data for one individual might look like this: T X t_0 True t_1 True t_2 False t_3 False t_4 True The observation times t_0, t_1, ... are irregularly spaced through time, and are not the same for each individual. I am interested in three survival outcomes for these data, but have so far only been able to look at the first two: Outcome 1. the time from t_0 until X becomes 'False' - this is simple survival analysis with an interval censored survival time, in this example the outcome occurs in the interval (t_1, t_2), and i can use SAS or s-plus to fit a variety of parametric survival models allowing for left/right/interval censoring. The log-likelihood contribution for the example individual above would be S(t_2) - S(t_1), where S(.) represents the survivor function. Outcome 2. the time from t_0 until X becomes 'True' after being 'False' (so this is conditional on X being 'false' at some point after t_0). Again, this is interval censored survival data, and SAS or s-plus can be used, although there is an issue about truncation if individuals who are always 'True' are excluded. Outcome 3. the time from X becoming 'False' until it becomes 'True' again - the "rebound" time. In this case, I know that the minimum possible rebound time is (t_3 - t_2), and the maximum possible rebound time is (t_4 - t_1), but how can i analyse these data? What about rescaling the time axis to allow this individual to contribute the term [ S(t_4 - t_1) - S(t_3 - t_2) ] to the log-likelihood? thanks for any help i'll summarise any responses and post to the list. Ben -- Ben Cowling Department of Infectious Disease Epidemiology Division of Primary Care and Population Health Sciences Faculty of Medicine Imperial College London Norfolk Place London W2 1PG email: [log in to unmask]