Comparison of Type I error rates and statistical power of different propensity score methods

DOI10.1080/00949655.2017.1406937OpenAlexW2773706829MaRDI QIDQ4960576

David T. Redden, Jeff M. Szychowski, Derek Dubay, Janice L. Case, Falynn C. Turley, Charles R. Katholi

Publication date: 23 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2017.1406937

zbMATH Keywords

powerlogistic regressionconfoundingtype I errorpropensity score

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• Binnor

Cites Work

• Propensity scores: From naïve enthusiasm to intuitive understanding
• The central role of the propensity score in observational studies for causal effects
• Model-Based Direct Adjustment
• Matching Using Estimated Propensity Scores: Relating Theory to Practice
• BinNor: An R Package for Concurrent Generation of Binary and Normal Data
• Introduction to variance estimation

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