ArticleQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2022
What difference does multiple imputation make in longitudinal modeling of EQ-5D-5L data? Empirical analyses of simulated and observed missing data patterns.
Article in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
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- Measuring the health outcomes of Chinese ischemic stroke patients based on the data from a longitudinal multi-center study.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Observational
- Trajectory of health-related quality of life during and after hospitalisation due to worsening of heart failure.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Quality of life and quality-adjusted life years after stroke in Sierra Leone.International journal of stroke : official journal of the International Stroke Society · 2024Article
- Factors Influencing Health-Related Quality of Life of Patients with Spinocerebellar Ataxia.Cerebellum (London, England) · 2024Article
- Assessing validity of the EQ-5D-5L proxy in children and adolescents with Duchenne muscular dystrophy or spinal muscular atrophy.The European journal of health economics : HEPAC : health economics in prevention and care · 2024Article
- Sequential Multiple Imputation for Real-World Health-Related Quality of Life Missing Data after Bariatric Surgery.International journal of environmental research and public health · 2022Article
- What difference does multiple imputation make in longitudinal modeling of EQ-5D-5L data? Empirical analyses of simulated and observed missing data patterns.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022Article
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Abstract
purposeAlthough multiple imputation is the state-of-the-art method for managing missing data, mixed models without multiple imputation may be equally valid for longitudinal data. Additionally, it is not clear whether missing values in multi-item instruments should be imputed at item or score-level. We therefore explored the differences in analyzing the scores of a health-related quality of life questionnaire (EQ-5D-5L) using four approaches in two empirical datasets.
methodsWe used simulated (GR dataset) and observed missingness patterns (ABCD dataset) in EQ-5D-5L scores to investigate the following approaches: approach-1) mixed models using respondents with complete cases, approach-2) mixed models using all available data, approach-3) mixed models after multiple imputation of the EQ-5D-5L scores, and approach-4) mixed models after multiple imputation of EQ-5D 5L items.
resultsApproach-1 yielded the highest estimates of all approaches (ABCD, GR), increasingly overestimating the EQ-5D-5L score with higher percentages of missing data (GR). Approach-4 produced the lowest scores at follow-up evaluations (ABCD, GR). Standard errors (0.006-0.008) and mean squared errors (0.032-0.035) increased with increasing percentages of simulated missing GR data. Approaches 2 and 3 showed similar results (both datasets).
conclusionComplete cases analyses overestimated the scores and mixed models after multiple imputation by items yielded the lowest scores. As there was no loss of accuracy, mixed models without multiple imputation, when baseline covariates are complete, might be the most parsimonious choice to deal with missing data. However, multiple imputation may be needed when baseline covariates are missing and/or more than two timepoints are considered.
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