TY - JOUR
T1 - The handling of missing data in trial-based economic evaluations
T2 - should data be multiply imputed prior to longitudinal linear mixed-model analyses?
AU - Ben, Ângela Jornada
AU - van Dongen, Johanna M.
AU - Alili, Mohamed El
AU - Heymans, Martijn W.
AU - Twisk, Jos W.R.
AU - MacNeil-Vroomen, Janet L.
AU - de Wit, Maartje
AU - van Dijk, Susan E.M.
AU - Oosterhuis, Teddy
AU - Bosmans, Judith E.
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2023/8
Y1 - 2023/8
N2 - Introduction: For the analysis of clinical effects, multiple imputation (MI) of missing data were shown to be unnecessary when using longitudinal linear mixed-models (LLM). It remains unclear whether this also applies to trial-based economic evaluations. Therefore, this study aimed to assess whether MI is required prior to LLM when analyzing longitudinal cost and effect data. Methods: Two-thousand complete datasets were simulated containing five time points. Incomplete datasets were generated with 10, 25, and 50% missing data in follow-up costs and effects, assuming a Missing At Random (MAR) mechanism. Six different strategies were compared using empirical bias (EB), root-mean-squared error (RMSE), and coverage rate (CR). These strategies were: LLM alone (LLM) and MI with LLM (MI-LLM), and, as reference strategies, mean imputation with LLM (M-LLM), seemingly unrelated regression alone (SUR-CCA), MI with SUR (MI-SUR), and mean imputation with SUR (M-SUR). Results: For costs and effects, LLM, MI-LLM, and MI-SUR performed better than M-LLM, SUR-CCA, and M-SUR, with smaller EBs and RMSEs as well as CRs closers to nominal levels. However, even though LLM, MI-LLM and MI-SUR performed equally well for effects, MI-LLM and MI-SUR were found to perform better than LLM for costs at 10 and 25% missing data. At 50% missing data, all strategies resulted in relatively high EBs and RMSEs for costs. Conclusion: LLM should be combined with MI when analyzing trial-based economic evaluation data. MI-SUR is more efficient and can also be used, but then an average intervention effect over time cannot be estimated.
AB - Introduction: For the analysis of clinical effects, multiple imputation (MI) of missing data were shown to be unnecessary when using longitudinal linear mixed-models (LLM). It remains unclear whether this also applies to trial-based economic evaluations. Therefore, this study aimed to assess whether MI is required prior to LLM when analyzing longitudinal cost and effect data. Methods: Two-thousand complete datasets were simulated containing five time points. Incomplete datasets were generated with 10, 25, and 50% missing data in follow-up costs and effects, assuming a Missing At Random (MAR) mechanism. Six different strategies were compared using empirical bias (EB), root-mean-squared error (RMSE), and coverage rate (CR). These strategies were: LLM alone (LLM) and MI with LLM (MI-LLM), and, as reference strategies, mean imputation with LLM (M-LLM), seemingly unrelated regression alone (SUR-CCA), MI with SUR (MI-SUR), and mean imputation with SUR (M-SUR). Results: For costs and effects, LLM, MI-LLM, and MI-SUR performed better than M-LLM, SUR-CCA, and M-SUR, with smaller EBs and RMSEs as well as CRs closers to nominal levels. However, even though LLM, MI-LLM and MI-SUR performed equally well for effects, MI-LLM and MI-SUR were found to perform better than LLM for costs at 10 and 25% missing data. At 50% missing data, all strategies resulted in relatively high EBs and RMSEs for costs. Conclusion: LLM should be combined with MI when analyzing trial-based economic evaluation data. MI-SUR is more efficient and can also be used, but then an average intervention effect over time cannot be estimated.
KW - Computer simulation
KW - Cost–benefit analysis
KW - Epidemiologic methods
KW - Longitudinal studies
UR - http://www.scopus.com/inward/record.url?scp=85138773346&partnerID=8YFLogxK
U2 - 10.1007/s10198-022-01525-y
DO - 10.1007/s10198-022-01525-y
M3 - Article
C2 - 36161553
SN - 1618-7598
VL - 24
SP - 951
EP - 965
JO - European Journal of Health Economics
JF - European Journal of Health Economics
IS - 6
ER -