What difference does multiple imputation make in longitudinal modeling of EQ-5D-5L data? Empirical analyses of simulated and observed missing data patterns

• Published in: Quality of life research Vol. 31; no. 5; pp. 1521 - 1532
• Main Authors: Rösel, Inka, Serna-Higuita, Lina María, Al Sayah, Fatima, Buchholz, Maresa, Buchholz, Ines, Kohlmann, Thomas, Martus, Peter, Feng, You-Shan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-05-2022; Springer Nature B.V
• Subjects: Datasets; Humans; Longitudinal studies; Medicine; Medicine & Public Health; Missing data; Psychometrics - methods; Public Health; Quality of life; Quality of Life - psychology; Quality of Life Research; Rehabilitation; Research Design; Simulation; Sociology; Surveys and Questionnaires; Germany; Missing data; EQ-5D; Health-related quality of life; Imputation; Missing at random
• ISSN: 0962-9343; 1573-2649; 1573-2649
• DOI: 10.1007/s11136-021-03037-3

Purpose
Although 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.
Methods
We 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.
Results
Approach-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).
Conclusion
Complete 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.