Missing.... presumed at random: cost-analysis of incomplete data

Briggs, A. , Clark, T., Wolstenholme, J. and Clarke, P. (2002) Missing.... presumed at random: cost-analysis of incomplete data. Health Economics, 12(5), pp. 377-392. (doi: 10.1002/hec.766)



Publisher's URL: http://dx.doi.org/10.1002/hec.766


When collecting patient-level resource use data for statistical analysis, for some patients and in some categories of resource use, the required count will not be observed. Although this problem must arise in most reported economic evaluations containing patient-level data, it is rare for authors to detail how the problem was overcome. Statistical packages may default to handling missing data through a so-called complete case analysis, while some recent cost-analyses have appeared to favour an available case approach. Both of these methods are problematic: complete case analysis is inefficient and is likely to be biased; available case analysis, by employing different numbers of observations for each resource use item, generates severe problems for standard statistical inference. Instead we explore imputation methods for generating replacement values for missing data that will permit complete case analysis using the whole data set and we illustrate these methods using two data sets that had incomplete resource use information.

Item Type:Articles
Keywords:Economic evaluation, cost-analysis, missing data.
Glasgow Author(s) Enlighten ID:Briggs, Professor Andrew
Authors: Briggs, A., Clark, T., Wolstenholme, J., and Clarke, P.
Subjects:R Medicine > RA Public aspects of medicine
H Social Sciences > HG Finance
College/School:College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Health Economics and Health Technology Assessment
College of Medical Veterinary and Life Sciences
Journal Name:Health Economics
Copyright Holders:Copyright © 2002 Wiley
First Published:First published in Health Economics 12(5):377-392
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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