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

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.