A Bayesian method for adverse effects estimation in observational studies with truncation by death.

Death among subjects is common in observational studies evaluating the causal effects of interventions among geriatric or severely ill patients. High mortality rates complicate the comparison of the prevalence of adverse events between interventions. This problem is often referred to as outcome "truncation" by death. A possible solution is to estimate the survivor average causal effect, an estimand that evaluates the effects of interventions among those who would have survived under both treatment assignments. However, because the survivor average causal effect does not include subjects who would have died under one or both arms, it does not consider the relationship between adverse events and death. We propose a Bayesian method which imputes the unobserved mortality and adverse event outcomes for each participant under the intervention they did not receive. Using the imputed outcomes we define a composite ordinal outcome for each patient, combining the occurrence of death and the adverse event in an increasing scale of severity. This allows for the comparison of the effects of the interventions on death and the adverse event simultaneously among the entire sample. We implement the procedure to analyze the incidence of heart failure among geriatric patients being treated for Type II diabetes with sulfonylureas or dipeptidyl peptidase-4 inhibitors.