Addressing unmeasured confounding in comparative observational research. Issue 4 (30th January 2018)

• Record Type: Journal Article
• Authors: Zhang, Xiang; Faries, Douglas E.; Li, Hu; Stamey, James D.; Imbens, Guido W.
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 27:Issue 4(2018)
• Page Start: 373
• Page End: 382
• Publication Date: 2018-01-30
• Subjects: best practice recommendation -- pharmacoepidemiology -- sensitivity analyses -- systematic review -- unmeasured confounding; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.4394
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: Purpose: Observational pharmacoepidemiological studies can provide valuable information on the effectiveness or safety of interventions in the real world, but one major challenge is the existence of unmeasured confounder(s). While many analytical methods have been developed for dealing with this challenge, they appear under‐utilized, perhaps due to the complexity and varied requirements for implementation. Thus, there is an unmet need to improve understanding the appropriate course of action to address unmeasured confounding under a variety of research scenarios. Methods: We implemented a stepwise search strategy to find articles discussing the assessment of unmeasured confounding in electronic literature databases. Identified publications were reviewed and characterized by the applicable research settings and information requirements required for implementing each method. We further used this information to develop a best practice recommendation to help guide the selection of appropriate analytical methods for assessing the potential impact of unmeasured confounding. Results: Over 100 papers were reviewed, and 15 methods were identified. We used a flowchart to illustrate the best practice recommendation which was driven by 2 critical components: (1) availability of information on the unmeasured confounders; and (2) goals of the unmeasured confounding assessment. Key factors for implementation of each method were summarized in a checklist to provide further assistance to researchers for implementing these methods. Conclusion: When assessing comparative effectiveness or safety in observational research, the impact of unmeasured confounding should not be ignored. Instead, we suggest quantitatively evaluating the impact of unmeasured confounding and provided a best practice recommendation for selecting appropriate analytical methods.