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Review of statistical methods for analysing healthcare resources and costs

Health economics, 2011-08, Vol.20 (8), p.897-916 [Peer Reviewed Journal]

Copyright © 2010 John Wiley & Sons, Ltd. ;Copyright Wiley Periodicals Inc. Aug 2011 ;Copyright © 2010 John Wiley & Sons, Ltd. 2010 ;ISSN: 1057-9230 ;EISSN: 1099-1050 ;DOI: 10.1002/hec.1653 ;PMID: 20799344

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  • Title:
    Review of statistical methods for analysing healthcare resources and costs
  • Author: Mihaylova, Borislava ; Briggs, Andrew ; O'Hagan, Anthony ; Thompson, Simon G.
  • Subjects: Clinical trials ; Data Interpretation, Statistical ; Generalized linear models ; Health Care Costs ; Health care expenditures ; Health economics ; Health Resources - utilization ; healthcare costs ; healthcare resource use ; Linear Models ; Markov analysis ; Markov Chains ; randomised trials ; Randomized Controlled Trials as Topic ; Statistical methods ; Studies
  • Is Part Of: Health economics, 2011-08, Vol.20 (8), p.897-916
  • Description: We review statistical methods for analysing healthcare resource use and costs, their ability to address skewness, excess zeros, multimodality and heavy right tails, and their ease for general use. We aim to provide guidance on analysing resource use and costs focusing on randomised trials, although methods often have wider applicability. Twelve broad categories of methods were identified: (I) methods based on the normal distribution, (II) methods following transformation of data, (III) single‐distribution generalized linear models (GLMs), (IV) parametric models based on skewed distributions outside the GLM family, (V) models based on mixtures of parametric distributions, (VI) two (or multi)‐part and Tobit models, (VII) survival methods, (VIII) non‐parametric methods, (IX) methods based on truncation or trimming of data, (X) data components models, (XI) methods based on averaging across models, and (XII) Markov chain methods. Based on this review, our recommendations are that, first, simple methods are preferred in large samples where the near‐normality of sample means is assured. Second, in somewhat smaller samples, relatively simple methods, able to deal with one or two of above data characteristics, may be preferable but checking sensitivity to assumptions is necessary. Finally, some more complex methods hold promise, but are relatively untried; their implementation requires substantial expertise and they are not currently recommended for wider applied work. Copyright © 2010 John Wiley & Sons, Ltd.
  • Publisher: Chichester, UK: John Wiley & Sons, Ltd
  • Language: English
  • Identifier: ISSN: 1057-9230
    EISSN: 1099-1050
    DOI: 10.1002/hec.1653
    PMID: 20799344
  • Source: MEDLINE
    Alma/SFX Local Collection
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