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Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index?

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Abstract

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.

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Notes

  1. Missing data due to dropouts for informative reasons are cases when the participant fails to complete a questionnaire as a result of their severity of their illness, death or other known reason. Non-responders occur when the participant does not respond to a questionnaire at one or multiple time points, which creates different missing data patterns in a dataset. In cross-sectional studies, the main missing data patterns are unit non-response when the participant fails to complete all the items within a questionnaire, and item non-response when the participant fails to complete some of the items within a questionnaire. In longitudinal studies, the participant may drop out before the end of the study and do not return creating a monotone missing data pattern.

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Acknowledgments

We are indebted to the ISAT Collaborative Group for providing the data for this methodological work. ISAT was supported by grants from: The Medical Research Council, UK; Programme Hospitalier de Recherche Clinique 1998 of the French Ministry of Health (AOM 98150) sponsored by Assistance Publique-Hôpitaux de Paris (AP-HP); the Canadian Institutes of Health Research; and the Stroke Association of the UK. An early version of this paper was presented in the 83rd Health Economists’ Study Group (HESG) at the University of Warwick and we are grateful to Lazaros Andronis for discussing the manuscript and providing feedback and useful suggestions. This report is independent research arising from a NIHR Research Methods Fellowship, Claire Simons MET-12-15, supported by the National Institute for Health Research. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

Conflict of interest

Oliver Rivero-Arias discloses that he is a member of the EuroQol Research Foundation.

Ethical standard

All human studies have been approved by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All persons gave informed consent prior to their inclusion in the ISAT study.

Funding

The work reported in this article was not funded by a specific grant.

Author information

Authors and Affiliations

  1. Health Economics Research Centre (HERC), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK

    Claire L. Simons, Oliver Rivero-Arias & Judit Simon

  2. National Perinatal Epidemiology Unit (NPEU), Nuffield Department of Population Health, University of Oxford, Oxford, UK

    Oliver Rivero-Arias

  3. Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

    Ly-Mee Yu

  4. Department of Health Economics, Centre for Public Health, Medical University of Vienna, Vienna, Austria

    Judit Simon

Authors
  1. Claire L. Simons
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  2. Oliver Rivero-Arias
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Correspondence to Oliver Rivero-Arias.

Additional information

Claire L. Simons and Oliver Rivero-Arias are joint lead authors.

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Simons, C.L., Rivero-Arias, O., Yu, LM. et al. Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index?. Qual Life Res 24, 805–815 (2015). https://doi.org/10.1007/s11136-014-0837-y

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