Triple challenges - Small sample size in both exposure and control groups to scan rare maternal outcomes in a signal identification approach: A simulation study.

Autor:	Thai TN; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA.; Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL.; Center for Drug Evaluation and Safety, University of Florida, Gainesville, FL., Winterstein AG; Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL.; Center for Drug Evaluation and Safety, University of Florida, Gainesville, FL.; Department of Epidemiology, College of Medicine and College of Public Health and Health Professions, University of Florida, Gainesville, FL., Suarez EA; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA., He J; Office of Biostatistics, Center for Drug Evaluation and Research, FDA, Silver Spring, MD., Zhao Y; Office of Biostatistics, Center for Drug Evaluation and Research, FDA, Silver Spring, MD., Zhang D; Office of Biostatistics, Center for Drug Evaluation and Research, FDA, Silver Spring, MD., Stojanovic D; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD., Liedtka J; Division of Pediatric and Maternal Health, Center for Drug and Evaluation Research, US Food and Drug Administration, Silver Spring, MD., Anderson A; Division of Urology, Obstetrics and Gynecology, Center for Drug and Evaluation Research, US Food and Drug Administration, Silver Spring, MD., Hernández-Muñoz JJ; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD., Munoz M; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD., Liu W; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD., Dashevsky I; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA., Messenger-Jones E; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA., Siranosian E; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA., Maro JC; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA.
Jazyk:	angličtina
Zdroj:	American journal of epidemiology [Am J Epidemiol] 2024 Jun 24. Date of Electronic Publication: 2024 Jun 24.
DOI:	10.1093/aje/kwae151
Abstrakt:	There is a dearth of safety data on maternal outcomes after perinatal medication exposure. Data-mining for unexpected adverse event occurrence in existing datasets is a potentially useful approach. One method, the Poisson tree-based scan statistic (TBSS), assumes that the expected outcome counts, based on incidence of outcomes in the control group, are estimated without error. This assumption may be difficult to satisfy with a small control group. Our simulation study evaluated the effect of imprecise incidence proportions from the control group on TBSS' ability to identify maternal outcomes in pregnancy research. We simulated base case analyses with "true" expected incidence proportions and compared these to imprecise incidence proportions derived from sparse control samples. We varied parameters impacting Type I error and statistical power (exposure group size, outcome's incidence proportion, and effect size). We found that imprecise incidence proportions generated by a small control group resulted in inaccurate alerting, inflation of Type I error, and removal of very rare outcomes for TBSS analysis due to "zero" background counts. Ideally, the control size should be at least several times larger than the exposure size to limit the number of false positive alerts and retain statistical power for true alerts. (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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