title: Methods to control for unmeasured confounding in pharmacoepidemiology: an overview.
abstract: 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.
authors.affiliation: Department of Statistics (Biostatistics and Epidemiology), Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh. jamal-sta@sust.edu.|Division of Pharmacoepidemiology and Clinical Pharmacology, University of Utrecht, Utrecht, The Netherlands. jamal-sta@sust.edu.
authors.forename: Md Jamal
authors.initials: MJ
authors.lastname: Uddin
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Rolf H H
authors.initials: RH
authors.lastname: Groenwold
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Mohammed Sanni
authors.initials: MS
authors.lastname: Ali
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, University of Utrecht, Utrecht, The Netherlands.
authors.forename: Anthonius
authors.initials: A
authors.lastname: de Boer
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Kit C B
authors.initials: KC
authors.lastname: Roes
authors.affiliation: Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, USA.
authors.forename: Muhammad A B
authors.initials: MA
authors.lastname: Chowdhury
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, University of Utrecht, Utrecht, The Netherlands.
authors.forename: Olaf H
authors.initials: OH
authors.lastname: Klungel
mesh_terms: D006801:Humans; D017891:Pharmacoepidemiology / Q000379:methods*; D012107:Research Design; D013223:Statistics as Topic / Q000379:methods
keywords: Observational studies; Pharmacoepidemiology; Residual confounding; Review; Statistical methods; Unmeasured confounding; Unobserved confounding