Can statistical linkage of missing variables reduce bias in treatment effect estimates in comparative effectiveness research studies?
| Autor: | Jason P. Swindle, William H. Crown, Jessica Chang, Paul Buzinec, Nilay Shah, Kristijan H. Kahler, M. Olson, Bijan J. Borah |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Research design
Linkage (software) Comparative Effectiveness Research Models Statistical Databases Factual business.industry Health Policy Comparative effectiveness research Missing data Hepatitis C Variable (computer science) Outcome variable Bias Research Design Statistics Econometrics Electronic Health Records Humans Medicine Observational study Treatment effect business Retrospective Studies |
| Zdroj: | Journal of Comparative Effectiveness Research. 4:455-463 |
| ISSN: | 2042-6313 2042-6305 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|