A Precision Mixture Risk Model to Identify Adverse Drug Events in Subpopulations Using a Case-Crossover Design.

Autor: Shi Y; Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA., Eadon MT; Department of Medicine, Division of Nephrology, Indiana University School of Medicine, Indianapolis, Indiana, USA., Chen Y; Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA., Sun A; Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA., Yang Y; Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA., Chiang C; Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA., Donneyong M; College of Pharmacy, The Ohio State University, Columbus, Ohio, USA., Su J; Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA., Zhang P; Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2024 Nov 30; Vol. 43 (27), pp. 5088-5099. Date of Electronic Publication: 2024 Sep 19.
DOI: 10.1002/sim.10216
Abstrakt: Despite the success of pharmacovigilance studies in detecting signals of adverse drug events (ADEs) from real-world data, the risks of ADEs in subpopulations warrant increased scrutiny to prevent them in vulnerable individuals. Recently, the case-crossover design has been implemented to leverage large-scale administrative claims data for ADE detection, while controlling both observed confounding effects and short-term fixed unobserved confounding effects. Additionally, as the case-crossover design only includes cases, subpopulations can be conveniently derived. In this manuscript, we propose a precision mixture risk model (PMRM) to identify ADE signals from subpopulations under the case-crossover design. The proposed model is able to identify signals from all ADE-subpopulation-drug combinations, while controlling for false discovery rate (FDR) and confounding effects. We applied the PMRM to an administrative claims data. We identified ADE signals in subpopulations defined by demographic variables, comorbidities, and detailed diagnosis codes. Interestingly, certain drugs were associated with a higher risk of ADE only in subpopulations, while these drugs had a neutral association with ADE in the general population. Additionally, the PMRM could control FDR at a desired level and had a higher probability to detect true ADE signals than the widely used McNemar's test. In conclusion, the PMRM is able to identify subpopulation-specific ADE signals from a tremendous number of ADE-subpopulation-drug combinations, while controlling for both FDR and confounding effects.
(© 2024 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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