Comparative evaluation of methods approximating drug prescription durations in claims data: modeling, simulation, and application to real data
Rattachement africain : de. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
PURPOSE: The purpose of this study was to compare the predictive accuracy of different methods suggested for approximation of drug prescription durations in claims data. METHODS: We expanded a well-established modeling and simulation framework to compare approximated drug prescription durations with 'true' (i.e., simulated) durations. Real claims data of persons aged ≥65 years insured by the German nationwide 'Statutory Health Insurance Fund' AOK between 2010 and 2012 provided empiric input parameters that were completed with missing information on actual dosing patterns from an observational cohort. The distinct approximation methods were based on crude measures (one tablet a day), population-averaged measures (defined daily doses), or individually-derived measures (longitudinal coverage approximation of the applied dose, COV). As a proof-of-principle, we assessed the methods' performance to predict the well-characterized bleeding risks of anticoagulant, antiplatelet, and/or non-steroidal anti-inflammatory drugs. RESULTS: When applied to modeling and simulation data sets, the closest, least biased, and thus most accurate approximation was observed using the COV approximation. In a real-data example, rather similar results to an external reference were obtained for all methods. However, some of the differences between methods were meaningful, and the most reasonable and consistent results were obtained with the COV approach. CONCLUSION: Based on theoretically most accurate approximations and practically reasonable estimates, the individual COV approach was preferable over the population-averaged defined daily dose technique, although the latter might be justified in certain situations. Advantages of the COV approach are expected to be even bigger for drug therapies with particularly large dosing heterogeneity. Copyright © 2016 John Wiley & Sons, Ltd.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Comparative evaluation of methods approximating drug prescription durations in claims data: modeling, simulation, and application to real data
- Date Crossref
- 16/09/2016
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Heidelberg University pays non établi dans la noticeUniversité ou école supérieure
-
Universität Hamburg pays non établi dans la noticeUniversité ou école supérieure
-
University Medical Center Hamburg-Eppendorf Department of Health Economics and Health Services Research pays non établi dans la noticeÉtablissement de santé
-
Wissenschaftliches Institut der AOK pays non établi dans la noticeStructure de recherche
-
German Cancer Research Center pays non établi dans la noticeStructure de recherche
-
Department of Clinical Pharmacology and Pharmacoepidemiology University of Heidelberg Heidelberg Germany Department of Clinical Pharmacology and Pharmacoepidemiology pays non établi dans la noticeUniversité ou école supérieure
-
Hamburg Center for Health Economics University Medical Center Hamburg‐Eppendorf Hamburg Germany Department of Health Economics and Health Services Research pays non établi dans la noticeUniversité ou école supérieure
-
Network Aging Research University of Heidelberg Heidelberg Germany pays non établi dans la noticeUniversité ou école supérieure
-
Division of Clinical Epidemiology and Aging Research pays non établi dans la noticeÉtablissement de santé
Heidelberg University, Universität Hamburg et Department of Health Economics and Health Services Research — University Medical Center Hamburg-Eppendorf, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.