Can statistical linkage of missing variables reduce bias in treatment effect estimates in comparative effectiveness research studies?. (September 2015)

• Record Type: Journal Article
• Authors: Crown, William; Chang, Jessica; Olson, Melvin; Kahler, Kristijan; Swindle, Jason; Buzinec, Paul; Shah, Nilay; Borah, Bijan
• Is Part Of: Journal of comparative effectiveness research. Volume 4:Number 5(2015)
• Page Start: 455
• Page End: 463
• Publication Date: 2015-09
• Subjects: claims analysis -- comparative effectiveness research -- electronic medical records -- missing variable bias -- retrospective database studies -- statistical linkage; Medical care -- Research -- Periodicals; Medical care -- Research -- Evaluation -- Periodicals; Medical care -- Decision making -- Periodicals; Evidence-based medicine -- Periodicals; Medicine, Comparative -- Periodicals; 362.1072
• DOI: 10.2217/cer.15.23
• Languages: English
• ISSNs: 2042-6305

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.