Methods for time‐varying exposure related problems in pharmacoepidemiology: An overview. Issue 2 (28th December 2017)

• Record Type: Journal Article
• Authors: Pazzagli, Laura; Linder, Marie; Zhang, Mingliang; Vago, Emese; Stang, Paul; Myers, David; Andersen, Morten; Bahmanyar, Shahram
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 27:Issue 2(2018)
• Page Start: 148
• Page End: 160
• Publication Date: 2017-12-28
• Subjects: cumulative exposure and latency -- pharmacoepidemiology -- time‐varying confounders -- time‐varying exposure -- treatment episodes -- treatment switching; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.4372
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: Purpose: Lack of control for time‐varying exposures can lead to substantial bias in estimates of treatment effects. The aim of this study is to provide an overview and guidance on some of the available methodologies used to address problems related to time‐varying exposure and confounding in pharmacoepidemiology and other observational studies. The methods are explored from a conceptual rather than an analytical perspective. Methods: The methods described in this study have been identified exploring the literature concerning to the time‐varying exposure concept and basing the search on four fundamental pharmacoepidemiological problems, construction of treatment episodes, time‐varying confounders, cumulative exposure and latency, and treatment switching. Results: A correct treatment episodes construction is fundamental to avoid bias in treatment effect estimates. Several methods exist to address time‐varying covariates, but the complexity of the most advanced approaches—eg, marginal structural models or structural nested failure time models—and the lack of user‐friendly statistical packages have prevented broader adoption of these methods. Consequently, simpler methods are most commonly used, including, for example, methods without any adjustment strategy and models with time‐varying covariates. The magnitude of exposure needs to be considered and properly modelled. Conclusions: Further research on the application and implementation of the most complex methods is needed. Because different methods can lead to substantial differences in the treatment effect estimates, the application of several methods and comparison of the results is recommended. Treatment episodes estimation and exposure quantification are key parts in the estimation of treatment effects or associations of interest.