Causal Inference for Non-mortality Outcomes in the Presence of Death
Evaluation of the causal effect of a baseline exposure on a morbidity outcome at a fixed time point is often complicated when study participants die before morbidity outcomes are measured. In this setting, the causal effect is only well-defined for the principal stratum of subjects who would live whatever be the exposure. Motivated by gerontologic researchers interested in understanding the causal effect of vision loss on depression in a population with a high mortality rate, we introduce a set of scientifically driven assumptions to identify the causal effect among those who would live both with and without vision loss. Since the assumptions are non-identifiable, we embed our methodology within a sensitivity analysis framework. We apply our method using the first three rounds of survey data from the Salisbury Eye Evaluation, a population-based cohort study of older adults. We also present a simulation study that validates our method.