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임상에서 발생할 수 있는 문제 상황에서의 성향 점수 가중치 방법에 대한 비교 모의실험 연구
정시성,민은정 한국통계학회 2023 응용통계연구 Vol.36 No.5
The most representative design used in clinical trials is randomization, which is used to accurately estimate the treatment eect. However, comparison between the treatment group and the control group in an observational study without randomization is biased due to various unadjusted dierences, such as characteristics between patients. Propensity score weighting is a widely used method to address these problems and to minimize bias by adjusting those confounding and assess treatment eects. Inverse probability weighting, the most popular method, assigns weights that are proportional to the inverse of the conditional probability of receiving a specific treatment assignment, given observed covariates. However, this method is often suered by extreme propensity scores, resulting in biased estimates and excessive variance. Several alternative methods including trimming, overlap weights, and matching weights have been proposed to mitigate these issues. In this paper, we conduct a simulation study to compare performance of various propensity score weighting methods under diverse situation, such as limited overlap, misspecified propensity score, and treatment contrary to prediction. From the simulation results overlap weights and matching weights consistently outperform inverse probability weighting and trimming in terms of bias, root mean squared error and coverage probability.