COMPARATIVE EFFECTIVENESS OF PROPENSITY SCORE ESTIMATION METHODS FOR INVERSE PROBABILITY OF TREATMENT WEIGHTING ANALYSIS WITH COMPLEX SURVEY DATA: A SIMULATION STUDY.

ABSTRACT: Propensity score (PS) methods, including inverse probability of treatment weighting (IPTW) analysis, are increasingly applied to complex survey data in geriatric studies to infer causal effects. However, the comparative effectiveness of various PS estimation methods, particularly novel machine learning algorithms, has not been thoroughly explored when complex survey data are involved. We conducted a comprehensive simulation study to compare the following six PS estimation methods in IPTW analysis: Logistic Regression, Covariate Balancing Propensity Score, Generalized Boosted Model, Classification and Regression Tree, Random Forest (RF), and Super Learner. We considered 12 scenarios with varying treatment effects, degrees of non-linearity and non-additivity in the associations between covar

SUBMITTER: Li L 

PROVIDER: S-EPMC12721855 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature