title: Comparative evaluation of methods approximating drug prescription durations in claims data: modeling, simulation, and application to real data.
abstract: The purpose of this study was to compare the predictive accuracy of different methods suggested for approximation of drug prescription durations in claims data.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany.
authors.forename: Andreas D
authors.initials: AD
authors.lastname: Meid
authors.affiliation: Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
authors.forename: Dirk
authors.initials: D
authors.lastname: Heider
authors.affiliation: Wissenschaftliches Institut der AOK (WIdO), Berlin, Germany.
authors.forename: Jürgen-Bernhard
authors.initials: JB
authors.lastname: Adler
authors.affiliation: Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany.
authors.forename: Renate
authors.initials: R
authors.lastname: Quinzler
authors.affiliation: Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.|Network Aging Research, University of Heidelberg, Heidelberg, Germany.
authors.forename: Herrmann
authors.initials: H
authors.lastname: Brenner
authors.affiliation: Wissenschaftliches Institut der AOK (WIdO), Berlin, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Günster
authors.affiliation: Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
authors.forename: Hans-Helmut
authors.initials: HH
authors.lastname: König
authors.affiliation: Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany.
authors.forename: Walter E
authors.initials: WE
authors.lastname: Haefeli
mesh_terms: D000368:Aged; D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000008:administration & dosage / Q000009:adverse effects; D000925:Anticoagulants / Q000008:administration & dosage / Q000009:adverse effects; D015982:Bias; D003198:Computer Simulation; D005858:Germany; D006470:Hemorrhage / Q000139:chemically induced / Q000453:epidemiology; D006801:Humans; D007356:Insurance, Pharmaceutical Services / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D017891:Pharmacoepidemiology / Q000379:methods*; D010975:Platelet Aggregation Inhibitors / Q000008:administration & dosage / Q000009:adverse effects; D055553:Prescription Drugs / Q000008:administration & dosage* / Q000009:adverse effects; D015203:Reproducibility of Results; D013997:Time Factors
chemical_list: D000894:Anti-Inflammatory Agents, Non-Steroidal; D000925:Anticoagulants; D010975:Platelet Aggregation Inhibitors; D055553:Prescription Drugs
keywords: antithrombotic drugs; bleeding; claims data; drug interactions; drug prescription duration; non-steroidal anti-inflammatory drugs; pharmacoepidemiology