Document Detail

Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index?
MedLine Citation:
PMID:  25471286     Owner:  NLM     Status:  Publisher    
PURPOSE: Missing data are a well-known and widely documented problem in cost-effectiveness analyses alongside clinical trials using individual patient-level data. Current methodological research recommends multiple imputation (MI) to deal with missing health outcome data, but there is little guidance on whether MI for multi-attribute questionnaires, such as the EQ-5D-3L, should be carried out at domain or at summary score level. In this paper, we evaluated the impact of imputing individual domains versus imputing index values to deal with missing EQ-5D-3L data using a simulation study and developed recommendations for future practice.
METHODS: We simulated missing data in a patient-level dataset with complete EQ-5D-3L data at one point in time from a large multinational clinical trial (n = 1,814). Different proportions of missing data were generated using a missing at random (MAR) mechanism and three different scenarios were studied. The performance of using each method was evaluated using root mean squared error and mean absolute error of the actual versus predicted EQ-5D-3L indices.
RESULTS: In large sample sizes (n > 500) and a missing data pattern that follows mainly unit non-response, imputing domains or the index produced similar results. However, domain imputation became more accurate than index imputation with pattern of missingness following an item non-response. For smaller sample sizes (n < 100), index imputation was more accurate. When MI models were misspecified, both domain and index imputations were inaccurate for any proportion of missing data.
CONCLUSIONS: The decision between imputing the domains or the EQ-5D-3L index scores depends on the observed missing data pattern and the sample size available for analysis. Analysts conducting this type of exercises should also evaluate the sensitivity of the analysis to the MAR assumption and whether the imputation model is correctly specified.
Claire L Simons; Oliver Rivero-Arias; Ly-Mee Yu; Judit Simon
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-12-4
Journal Detail:
Title:  Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation     Volume:  -     ISSN:  1573-2649     ISO Abbreviation:  Qual Life Res     Publication Date:  2014 Dec 
Date Detail:
Created Date:  2014-12-4     Completed Date:  -     Revised Date:  2014-12-5    
Medline Journal Info:
Nlm Unique ID:  9210257     Medline TA:  Qual Life Res     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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