A Bayesian Method for Adverse Effects Estimation in Observational Studies with Truncation by Death
Anthony Sisti, Andrew Zullo, Roee Gutman

TL;DR
This paper introduces a Bayesian approach to estimate the effects of treatments on adverse events in observational studies with high mortality, by imputing unobserved outcomes and analyzing a combined severity scale.
Contribution
It develops a Bayesian method that jointly imputes mortality and adverse events, enabling comprehensive comparison of treatment effects on death and adverse events.
Findings
Method successfully applied to analyze heart failure incidence.
Imputed outcomes provide insights into treatment effects among all patients.
Composite severity scale facilitates simultaneous assessment of death and adverse events.
Abstract
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 (AEs) between interventions. This problem is often referred to as outcome "truncation" by death. A possible solution is to estimate the survivor average causal effect (SACE), an estimand that evaluates the effects of interventions among those who would have survived under both treatment assignments. However, because the SACE does not include subjects who would have died under one or both arms, it does not consider the relationship between AEs and death. We propose a Bayesian method which imputes the unobserved mortality and AE outcomes for each participant under the intervention they did not receive. Using the imputed outcomes we define a composite ordinal…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStatistical Methods in Clinical Trials · Statistical Methods and Inference · Advanced Causal Inference Techniques
