Cost-of-illness studies based on massive data: a prevalence-based, top-down regression approach

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• 作者：Björn Stollenwerk ; Thomas Welchowski…
• 关键词：Cost ; of ; illness ; Massive data ; Generalized additive models ; Error propagation
• 刊名：The European Journal of Health Economics
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：17
• 期：3
• 页码：235-244
• 全文大小：512 KB
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• 作者单位：Björn Stollenwerk (1)
  Thomas Welchowski (1) (2)
  Matthias Vogl (1)
  Stephanie Stock (3)
  
  1. Institute of Health Economics and Health Care Management, Helmholtz Zentrum München (GmbH), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
  2. Institut für Medizinische Biometrie, Informatik und Epidemiologie (IMBIE), Universitätsklinikum Bonn, Sigmund-Freud-Straße 25, 53105, Bonn, Germany
  3. Institute of Health Economics and Clinical Epidemiology, University of Cologne, Gleueler Straße 176-178, 50935, Cologne, Germany
  
• 刊物类别：Business and Economics
• 刊物主题：Economics
  Economic Policy
  Public Health
  Public Finance and Economics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1618-7601

文摘

Despite the increasing availability of routine data, no analysis method has yet been presented for cost-of-illness (COI) studies based on massive data. We aim, first, to present such a method and, second, to assess the relevance of the associated gain in numerical efficiency. We propose a prevalence-based, top-down regression approach consisting of five steps: aggregating the data; fitting a generalized additive model (GAM); predicting costs via the fitted GAM; comparing predicted costs between prevalent and non-prevalent subjects; and quantifying the stochastic uncertainty via error propagation. To demonstrate the method, it was applied to aggregated data in the context of chronic lung disease to German sickness funds data (from 1999), covering over 7.3 million insured. To assess the gain in numerical efficiency, the computational time of the innovative approach has been compared with corresponding GAMs applied to simulated individual-level data. Furthermore, the probability of model failure was modeled via logistic regression. Applying the innovative method was reasonably fast (19 min). In contrast, regarding patient-level data, computational time increased disproportionately by sample size. Furthermore, using patient-level data was accompanied by a substantial risk of model failure (about 80 % for 6 million subjects). The gain in computational efficiency of the innovative COI method seems to be of practical relevance. Furthermore, it may yield more precise cost estimates.