Methods to control for unmeasured confounding in pharmacoepidemiology: an overview

• Published in: International journal of clinical pharmacy Vol. 38; no. 3; pp. 714 - 723
• Main Authors: Uddin, Md. Jamal, Groenwold, Rolf H. H., Ali, Mohammed Sanni, de Boer, Anthonius, Roes, Kit C. B., Chowdhury, Muhammad A. B., Klungel, Olaf H.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-06-2016
• Subjects: Humans; Internal Medicine; Medicine; Medicine & Public Health; Pharmacoepidemiology - methods; Pharmacy; Research Design; Review Article; Statistics as Topic - methods; Pharmacoepidemiology; Statistical methods; Review; Unmeasured confounding; Residual confounding; Unobserved confounding; Observational studies
• ISSN: 2210-7703; 2210-7711
• DOI: 10.1007/s11096-016-0299-0

Background
Unmeasured confounding is one of the principal problems in pharmacoepidemiologic studies. Several methods have been proposed to detect or control for unmeasured confounding either at the study design phase or the data analysis phase.
Aim of the Review
To provide an overview of commonly used methods to detect or control for unmeasured confounding and to provide recommendations for proper application in pharmacoepidemiology.
Methods/Results
Methods to control for unmeasured confounding in the design phase of a study are case only designs (e.g., case-crossover, case-time control, self-controlled case series) and the prior event rate ratio adjustment method. Methods that can be applied in the data analysis phase include, negative control method, perturbation variable method, instrumental variable methods, sensitivity analysis, and ecological analysis. A separate group of methods are those in which additional information on confounders is collected from a substudy. The latter group includes external adjustment, propensity score calibration, two-stage sampling, and multiple imputation.
Conclusion
As the performance and application of the methods to handle unmeasured confounding may differ across studies and across databases, we stress the importance of using both statistical evidence and substantial clinical knowledge for interpretation of the study results.