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Missing.... presumed at random: cost‐analysis of incomplete data
Author(s) -
Briggs Andrew,
Clark Taane,
Wolstenholme Jane,
Clarke Philip
Publication year - 2003
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/hec.766
Subject(s) - missing data , imputation (statistics) , computer science , complete information , statistical inference , data mining , inference , resource (disambiguation) , data set , statistical analysis , set (abstract data type) , statistics , econometrics , machine learning , artificial intelligence , mathematics , computer network , mathematical economics , programming language
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. Copyright © 2002 John Wiley & Sons, Ltd.