Causal inference with longitudinal exposures when outcomes are subject to irregular and outcome-dependent observation: how multiple outputation can help

Music City Center 

Data collected in the context of usual care present a rich source of longitudinal data for research, but often require analyses that simultaneously enable causal inferences in the presence of treatment switching, and handle irregular and informative assessment times. Assessment not at random occurs when outcome and assessment process remain dependent on conditioning upon observed variables; approaches to causal inference in this context are limited. This talk will show how a process known as multiple outputation can be used to simultaneously handle longitudinal treatment confounding and a special case of assessment not at random, where assessment and outcome processes are conditionally independent given past observed covariates and random effects.

Longitudinal data

Causal inference