Propensity Score Matching: should we use it in designing observational studies?

ABSTRACT:

Background

Propensity Score Matching (PSM) stands as a widely embraced method in comparative effectiveness research. PSM crafts matched datasets, mimicking some attributes of randomized designs, from observational data. In a valid PSM design where all baseline confounders are measured and matched, the confounders would be balanced, allowing the treatment status to be considered as if it were randomly assigned. Nevertheless, recent research has unveiled a different facet of PSM, termed "the PSM paradox". As PSM approaches exact matching by progressively pruning matched sets in order of decreasing propensity score distance, it can paradoxically lead to greater covariate imbalance, heightened model dependence, and increased bias, contrary to its intended purpose.

Methods

We use

SUBMITTER: Wan F 

PROVIDER: S-EPMC11776168 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature