Sensitivity Analysis of Causal Treatment Effect Estimation for Clustered Observational Data with Unmeasured Confounding

TL;DR

This paper introduces a new sensitivity analysis method for clustered observational data that evaluates the robustness of causal treatment effect estimates against unmeasured confounders, applicable to both single and multiple studies.

Contribution

The paper presents a novel, flexible sensitivity analysis technique that imposes no restrictive assumptions on confounders and can handle various outcome types and study designs.

Findings

Methods perform well across diverse simulated scenarios.

No restrictive assumptions on number or relationship of confounders.

Easy implementation with standard statistical software.

Abstract

Identifying causal treatment (or exposure) effects in observational studies requires the data to satisfy the unconfoundedness assumption which is not testable using the observed data. With sensitivity analysis, one can determine how the conclusions might change if assumptions are violated to a certain degree. In this paper, we propose a new technique for sensitivity analysis applicable to clusters observational data with a normally distributed or binary outcome. The proposed methods aim to assess the robustness of estimated treatment effects in a single study as well as in multiple studies, i.e., meta-analysis, against unmeasured confounders. Simulations with various underlying scenarios were conducted to assess the performance of our methods. Unlike other existing sensitivity analysis methods, our methods have no restrictive assumptions on the number of unmeasured confounders or on the relationship between measured and unmeasured confounders, and do not exclude possible interactions between measured confounders and the treatment. Our methods are easy to implement using standard statistical software packages.