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Identifying causal treatment (or exposure) effects in observational studiesrequires the data to satisfy the unconfoundedness assumption which is nottestable using the observed data. With sensitivity analysis, one can determinehow the conclusions might change if assumptions are violated to a certaindegree. In this paper, we propose a new technique for sensitivity analysisapplicable to clusters observational data with a normally distributed or binaryoutcome. The proposed methods aim to assess the robustness of estimatedtreatment effects in a single study as well as in multiple studies, i.e.,meta-analysis, against unmeasured confounders. Simulations with variousunderlying scenarios were conducted to assess the performance of our methods.Unlike other existing sensitivity analysis methods, our methods have norestrictive assumptions on the number of unmeasured confounders or on therelationship between measured and unmeasured confounders, and do not excludepossible interactions between measured confounders and the treatment. Ourmethods are easy to implement using standard statistical software packages.

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