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Variable selection and raking in propensity scoring
Author(s) -
Judkins David R.,
Morganstein David,
Zador Paul,
Piesse Andrea,
Barrett Brandon,
Mukhopadhyay Pushpal
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2591
Subject(s) - covariate , propensity score matching , computer science , context (archaeology) , selection (genetic algorithm) , econometrics , statistics , measure (data warehouse) , machine learning , mathematics , data mining , biology , paleontology
This paper discusses some practical issues in applying propensity scoring in the context of endpoint analysis in a pre‐/posttest longitudinal design with an ordinal measure of treatment intensity and a high‐dimensional potential covariate space: how many covariates to include in propensity models; how to evaluate the adequacy of tentative propensity models; and how to tailor models to provide hypercontrol on a limited subset of covariates. These issues arose in the evaluation of a health communication program. Copyright © 2006 John Wiley & Sons, Ltd.