Work

A simulation study for various propensity score weighting methods in clinical problematic situations

Year: 2023

Type: article

Abstract: The most representative design used in clinical trials is randomization, which is used to accurately estimate the treatment effect.However, comparison between the treatment group and the control group in an observational study without randomization is biased due to various unadjusted differences, 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 effects.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 suffered 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.

Source: Korean Journal of Applied Statistics

DOI: 10.5351/kjas.2023.36.5.381

Authors: Siseong Jeong, Eun Jeong Min

Institution: Catholic University of Korea

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Research Foundation, Ministry of Science, ICT and Future Planning, National Research Foundation of Korea