The impact of directly observed therapy on the efficacy of Tuberculosis treatment: a Bayesian multilevel approach
提出一种贝叶斯方法，用于在存在混杂因素的多层次观测数据中估计因果效应，并以直接观察疗法对结核病治疗成功的影响为例，通过模拟研究说明在结果和倾向得分模型中纳入潜在局部随机效应可减少估计偏差。
Abstract We propose and discuss a Bayesian procedure to estimate causal effects for multilevel observations in the presence of confounding. This work is motivated by an interest in determining the causal impact of directly observed therapy on the successful treatment of Tuberculosis. We focus on propensity score regression and covariate adjustment to balance the treatment allocation. We discuss the need to include latent local-level random effects in the propensity score model to reduce bias in the estimation of causal effects. A simulation study suggests that accounting for the multilevel nature of the data with latent structures in both the outcome and propensity score models has the potential to reduce bias in the estimation of causal effects.