This article is published in Pharmaceutical Statistics as a special issue on Focusing on the PSI Special Interest Groups, edited by John Stevens, Centre for Bayesian Statistics in Health Economics, ScHARR, Regent Court, 30 Regent Street, Sheffield, South Yorkshire, S1 4DA, UK.
Opportunities for minimization of confounding in observational research
Article first published online: 30 NOV 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Special Issue: Focusing on the PSI Special Interest Groups
Volume 10, Issue 6, pages 539–547, November/December 2011
How to Cite
Quartey, G., Feudjo-Tepie, M., Wang, J. and Kim, J. (2011), Opportunities for minimization of confounding in observational research. Pharmaceut. Statist., 10: 539–547. doi: 10.1002/pst.528
- Issue published online: 28 DEC 2011
- Article first published online: 30 NOV 2011
- Manuscript Accepted: 3 OCT 2011
- Manuscript Revised: 25 AUG 2011
- Manuscript Received: 1 JUL 2011
- case series;
- marginal structural model;
- instrumental variable;
- propensity score;
- selection model
Observational epidemiological studies are increasingly used in pharmaceutical research to evaluate the safety and effectiveness of medicines. Such studies can complement findings from randomized clinical trials by involving larger and more generalizable patient populations by accruing greater durations of follow-up and by representing what happens more typically in the clinical setting. However, the interpretation of exposure effects in observational studies is almost always complicated by non-random exposure allocation, which can result in confounding and potentially lead to misleading conclusions. Confounding occurs when an extraneous factor, related to both the exposure and the outcome of interest, partly or entirely explains the relationship observed between the study exposure and the outcome. Although randomization can eliminate confounding by distributing all such extraneous factors equally across the levels of a given exposure, methods for dealing with confounding in observational studies include a careful choice of study design and the possible use of advanced analytical methods. The aim of this paper is to introduce some of the approaches that can be used to help minimize the impact of confounding in observational research to the reader working in the pharmaceutical industry. Copyright © 2011 John Wiley & Sons, Ltd.