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Observational studies of recurrent event rates are common in biomedicalstatistics. Broadly, the goal is to estimate differences in event rates undertwo treatments within a defined target population over a specified followupwindow. Estimation with observational claims data is challenging because whilemembership in the target population is defined in terms of eligibilitycriteria, treatment is rarely assigned exactly at the time of eligibility.Ad-hoc solutions to this timing misalignment, such as assigning treatment ateligibility based on subsequent assignment, incorrectly attribute prior eventrates to treatment - resulting in immortal risk bias. Even if eligibility andtreatment are aligned, a terminal event process (e.g. death) often stops therecurrent event process of interest. Both processes are also censored so thatevents are not observed over the entire followup window. Our approach addressesmisalignment by casting it as a treatment switching problem: some patients areon treatment at eligibility while others are off treatment but may switch totreatment at a specified time - if they survive long enough. We define andidentify an average causal effect of switching under specified causalassumptions. Estimation is done using a g-computation framework with a jointsemiparametric Bayesian model for the death and recurrent event processes.Computing the estimand for various switching times allows us to assess theimpact of treatment timing. We apply the method to contrast hospitalizationrates under different opioid treatment strategies among patients with chronicback pain using Medicare claims data.

A Bayesian Framework for Causal Analysis of Recurrent Events in Presence of Immortal Risk