Can statistical linkage of missing variables reduce bias in treatment effect estimates in comparative effectiveness research studies?

William Crown, Jessica Chang, Melvin Olson, Kristijan Kahler, Jason Swindle, Paul Buzinec, Nilay D Shah, Bijan J Borah

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Aim: Missing data, particularly missing variables, can create serious analytic challenges in observational comparative effectiveness research studies. Statistical linkage of datasets is a potential method for incorporating missing variables. Prior studies have focused upon the bias introduced by imperfect linkage. Methods: This analysis uses a case study of hepatitis C patients to estimate the net effect of statistical linkage on bias, also accounting for the potential reduction in missing variable bias. Results: The results show that statistical linkage can reduce bias while also enabling parameter estimates to be obtained for the formerly missing variables. Conclusion: The usefulness of statistical linkage will vary depending upon the strength of the correlations of the missing variables with the treatment variable, as well as the outcome variable of interest.

Original languageEnglish (US)
Pages (from-to)455-463
Number of pages9
JournalJournal of Comparative Effectiveness Research
Volume4
Issue number5
DOIs
StatePublished - Sep 1 2015

Keywords

  • claims analysis
  • comparative effectiveness research
  • electronic medical records
  • missing variable bias
  • retrospective database studies
  • statistical linkage

ASJC Scopus subject areas

  • Health Policy

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