An omnibus approach to assess covariate balance in observational studies using the distance covariance

Adin Cristian Andrei*, Patrick M. McCarthy

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Adequate baseline covariate balance among groups is critical in observational studies designed to estimate causal effects. Propensity score-based methods are popular ways to achieve covariate balance among groups. Existing methods are not easily generalizable to situations in which covariates of mixed type are collected nor do they provide a convenient way to compare the overall covariate vector distributions. Instead, covariate balance is assessed at the individual covariate level, thus the potential for increased overall type I error. We propose the use of the distance covariance, developed by Székely and colleagues, as an omnibus test of independence between covariate vectors and study group. We illustrate the advantages of this methodology in simulated data and in a cardiac surgery study designed to assess the impact of preoperative statin therapy on outcomes.

Original languageEnglish (US)
JournalStatistical Methods in Medical Research
DOIs
StateAccepted/In press - Jan 1 2019

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Keywords

  • Causal inference
  • covariate vector balance
  • distance covariance
  • observational study
  • propensity score
  • statin therapy

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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