{"title":"Methodological Considerations on the Use of Cohort Designs in Drug-Drug Interaction Studies in Pharmacoepidemiology","authors":"Jenny Dimakos, Antonios Douros","doi":"10.1007/s40471-024-00347-1","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose of Review</h3><p>The evidence regarding the clinical effects of drug-drug interactions (DDIs) is scarce and limited. Pharmacoepidemiologic studies could help fill in this important knowledge gap. Here, we review the pharmacoepidemiology of DDIs with a focus on cohort designs. We also highlight the decision-making process with respect to different aspects of cohort study design, potential biases that may arise during this decision process, and mitigation strategies.</p><h3 data-test=\"abstract-sub-heading\">Recent Findings</h3><p>Considering the pharmacologic mechanism of the DDI of interest as well as of the object drug and the precipitant drug separately at the design stage of cohort studies for DDIs will help minimize major biases such as prevalent user bias and confounding by indication. Confounding by indication could also be mitigated by using control precipitants. Further, the correct assignment of the cohort entry date via the application of a time-varying exposure definition can help minimize immortal time bias and prevalent user bias. Minimization of these biases may also potentially be achieved with recently developed tools such as target trial emulation and the prevalent new-user design; however, more research is needed in the area.</p><h3 data-test=\"abstract-sub-heading\">Summary</h3><p>Careful consideration of the underlying pharmacology and the specifics of study design will help minimize major biases in cohort studies that aim to assess the clinical effects of DDIs. Recent methodological developments from other areas of pharmacoepidemiology could further improve the internal validity of DDI studies.</p>","PeriodicalId":48527,"journal":{"name":"Current Epidemiology Reports","volume":"125 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Epidemiology Reports","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40471-024-00347-1","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose of Review
The evidence regarding the clinical effects of drug-drug interactions (DDIs) is scarce and limited. Pharmacoepidemiologic studies could help fill in this important knowledge gap. Here, we review the pharmacoepidemiology of DDIs with a focus on cohort designs. We also highlight the decision-making process with respect to different aspects of cohort study design, potential biases that may arise during this decision process, and mitigation strategies.
Recent Findings
Considering the pharmacologic mechanism of the DDI of interest as well as of the object drug and the precipitant drug separately at the design stage of cohort studies for DDIs will help minimize major biases such as prevalent user bias and confounding by indication. Confounding by indication could also be mitigated by using control precipitants. Further, the correct assignment of the cohort entry date via the application of a time-varying exposure definition can help minimize immortal time bias and prevalent user bias. Minimization of these biases may also potentially be achieved with recently developed tools such as target trial emulation and the prevalent new-user design; however, more research is needed in the area.
Summary
Careful consideration of the underlying pharmacology and the specifics of study design will help minimize major biases in cohort studies that aim to assess the clinical effects of DDIs. Recent methodological developments from other areas of pharmacoepidemiology could further improve the internal validity of DDI studies.