Curr Epidemiol Rep (2017) 4:288–297 https://doi.org/10.1007/s40471-017-0124-x PHARMACOEPIDEMIOLOGY (S TOH, SECTION EDITOR) Estimating Effects of Dynamic Treatment Strategies in Pharmacoepidemiologic Studies with Time-Varying Confounding: a Primer 1 1 1 Xiaojuan Li & Jessica G. Young & Sengwee Toh Published online: 17 October 2017 Springer International Publishing AG 2017 Abstract the relatively complex analysis. These barriers may be over- Purpose of Review Pharmacoepidemiologists are often inter- come with the increasing availability of data sources contain- ested in estimating the effects of dynamic treatment strategies, ing more detailed time-varying information and more acces- where treatments are modified based on patients’ evolving sible learning tools and software. characteristics. For such problems, appropriate control of both Summary With appropriate data and study design, g-methods baseline and time-varying confounders is critical. can improve our ability to make causal inferences on dynamic Conventional methods that control confounding by including treatment strategies from observational data in pharma time-varying treatments and confounders in an outcome re- coepidemiology. gression model may not have a causal interpretation, even . . when all baseline and time-varying confounders are mea- Keywords Time-varying confounding G-methods . . sured. This problem occurs when time-varying confounders Pharmacoepidemiology Causal inference Dynamic are, themselves, affected by past
Current Epidemiology Reports – Springer Journals
Published: Oct 17, 2017
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