JOINTVIP: Prioritizing variables in observational study design with joint variable importance plot in R

Credible causal effect estimation requires treated subjects and controls to be otherwise similar. In observational settings, such as analysis of electronic health records, this is not guaranteed. Investigators must balance background variables so they are similar in treated and control groups. Commo...

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Bibliographic Details
Main Authors Liao, Lauren D, Pimentel, Samuel D
Format Journal Article
LanguageEnglish
Published 20.02.2023
Subjects
Statistics - Computation
Statistics - Methodology
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Summary:Credible causal effect estimation requires treated subjects and controls to be otherwise similar. In observational settings, such as analysis of electronic health records, this is not guaranteed. Investigators must balance background variables so they are similar in treated and control groups. Common approaches include matching (grouping individuals into small homogeneous sets) or weighting (upweighting or downweighting individuals) to create similar profiles. However, creating identical distributions may be impossible if many variables are measured, and not all variables are of equal importance to the outcome. The joint variable importance plot (jointVIP) package to guides decisions about which variables to prioritize for adjustment by quantifying and visualizing each variable's relationship to both treatment and outcome.
DOI:10.48550/arxiv.2302.10367