Krista F Huybrechts1, Brian T Bateman1,2, Sonia Hernández-Díaz3. 1. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. 2. Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. 3. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Abstract
PURPOSE: Because preapproval clinical trials typically exclude pregnant women, the evidence on drug safety during pregnancy required to inform drug labeling must come from postapproval controlled observational studies. Common designs have included pregnancy registries and case-control studies. Recently, pregnancy cohorts nested within healthcare utilization databases are increasingly being used. Despite clear advantages, these databases share some important limitations that may threaten the validity of studies emerging from them. METHODS: This paper describes the distinctive methodological aspects of conducting drug safety studies in healthcare utilization databases with special emphasis on design and analytic approaches to minimize biases. RESULTS: We describe considerations for study design, cohort definition, and follow-up. We then address issues related to exposure ascertainment based on prescription fills, including the importance of the etiologically relevant window and of properly accounting for preterm births. This is followed by a discussion of advantages and challenges when ascertaining maternal and infant outcomes based on secondary data. We then explore useful approaches to address confounding within the context of pregnancy research and of the potential for selection bias when restricting the cohort to live births. Finally, we consider issues related to external validity and statistical significance. The examples are mainly drawn from a pregnancy cohort nested in the Medicaid Analytic Extract. CONCLUSIONS: The approaches presented provide guidance regarding the important methodological considerations that need to be attended to in order to generate valid, minimally biased risk when using large healthcare utilization databases for drug safety surveillance in pregnancy.
PURPOSE: Because preapproval clinical trials typically exclude pregnant women, the evidence on drug safety during pregnancy required to inform drug labeling must come from postapproval controlled observational studies. Common designs have included pregnancy registries and case-control studies. Recently, pregnancy cohorts nested within healthcare utilization databases are increasingly being used. Despite clear advantages, these databases share some important limitations that may threaten the validity of studies emerging from them. METHODS: This paper describes the distinctive methodological aspects of conducting drug safety studies in healthcare utilization databases with special emphasis on design and analytic approaches to minimize biases. RESULTS: We describe considerations for study design, cohort definition, and follow-up. We then address issues related to exposure ascertainment based on prescription fills, including the importance of the etiologically relevant window and of properly accounting for preterm births. This is followed by a discussion of advantages and challenges when ascertaining maternal and infant outcomes based on secondary data. We then explore useful approaches to address confounding within the context of pregnancy research and of the potential for selection bias when restricting the cohort to live births. Finally, we consider issues related to external validity and statistical significance. The examples are mainly drawn from a pregnancy cohort nested in the Medicaid Analytic Extract. CONCLUSIONS: The approaches presented provide guidance regarding the important methodological considerations that need to be attended to in order to generate valid, minimally biased risk when using large healthcare utilization databases for drug safety surveillance in pregnancy.
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