Título: Bayesian estimation of the Average Treatment Effect for multilevel structured observations
Palestrante: Widemberg da Silva Nobre (DME/UFRJ)

Data:24/03/2021
Horário: 15:30h
Local: Transmissão online

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Resumo: We discuss Bayesian foundations for the estimation of the Average Treatment Effect (ATE) in the scenario of multilevel observations and in presence of confounding. Confounding occurs when a set of covariates (confounders) impact exposure and outcome simultaneously. In particular, we focus on scenarios when the set of confounders may include unobserved ones. We study the situation wherein multiple observations are made at a given location (e.g. individuals living across cities of a state). We explore the use of the propensity score approach through covariate adjustment to provide balancing of the treatment allocation (exposure). We discuss, through different simulation studies, the need to include location level random effects in the propensity score model to reduce bias in the estimation of the ATE. We also explore different prior specifications for the local level random effects. Our motivating example entails the effectiveness of the driven observed therapy (DOT) in the treatment of Tuberculosis (TB) for individuals who had TB across cities of the state of São Paulo, Brazil, in 2016. This is joint work with Alexandra M. Schmidt, Erica E. M. Moodie and David A. Stephens.