stratamatch: Prognostic Score Stratification Using a Pilot Design

Rachael C. Aikens, Joseph Rigdon, Justin Lee, Andrew B. Goldstone, Peter Chiu, Y. Joseph Woo, Jonathan H. Chen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Optimal propensity score matching has emerged as one of the most ubiquitous approaches for causal inference studies on observational data. However, outstanding critiques of the statistical properties of propensity score matching have cast doubt on the statistical efficiency of this technique, and the poor scalability of optimal matching to large data sets makes this approach inconvenient if not infeasible for sample sizes that are increasingly commonplace in modern observational data. The stratamatch package provides implementation support and diagnostics for ‘stratified matching designs,‘ an approach that addresses both of these issues with optimal propensity score matching for large-sample observational studies. First, stratifying the data enables more computationally efficient matching of large data sets. Second, stratamatch implements a ‘pilot design’ approach in order to stratify by a prognostic score, which may increase the precision of the effect estimate and increase power in sensitivity analyses of unmeasured confounding.

Original languageEnglish
Pages (from-to)614-630
Number of pages17
JournalR Journal
Volume13
Issue number1
DOIs
StatePublished - 2021
Externally publishedYes

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