TY - JOUR
T1 - Multiple imputation of systematically missing data on gait speed in the Swedish National Study on Aging and Care
AU - Thiesmeier, Robert
AU - Abbadi, Ahmad
AU - Rizzuto, Debora
AU - Calderón-Larrañaga, Amaia
AU - Hofer, Scott M.
AU - Orsini, Nicola
N1 - Publisher Copyright:
© 2024 Thiesmeier et al.
PY - 2024
Y1 - 2024
N2 - Background: There is insufficient investigation of multiple imputation for systematically missing discrete variables in individual participant data meta-analysis (IPDMA) with a small number of included studies. Therefore, this study aims to evaluate the performance of three multiple imputation strategies – fully conditional specification (FCS), multivariate normal (MVN), conditional quantile imputation (CQI) – on systematically missing data on gait speed in the Swedish National Study on Aging and Care (SNAC). Methods: In total, 1 000 IPDMA were simulated with four prospective cohort studies based on the characteristics of the SNAC. The three multiple imputation strategies were analysed with a two-stage common-effect multivariable logistic model targeting the effect of three levels of gait speed (100% missing in one study) on 5-years mortality with common odds ratios set to OR1 = 0.55 (0.8-1.2 vs ≤0.8 m/s), and OR2 = 0.29 (>1.2 vs ≤0.8 m/s). Results: The average combined estimate for the mortality odds ratio OR1 (relative bias %) were 0.58 (8.2%), 0.58 (7.5%), and 0.55 (0.7%) for the FCS, MVN, and CQI, respectively. The average combined estimate for the mortality odds ratio OR2 (relative bias %) were 0.30 (2.5%), 0.33 (10.0%), and 0.29 (0.9%) for the FCS, MVN, and CQI respectively. Conclusions: In our simulations of an IPDMA based on the SNAC where gait speed data was systematically missing in one study, all three imputation methods performed relatively well. The smallest bias was found for the CQI approach.
AB - Background: There is insufficient investigation of multiple imputation for systematically missing discrete variables in individual participant data meta-analysis (IPDMA) with a small number of included studies. Therefore, this study aims to evaluate the performance of three multiple imputation strategies – fully conditional specification (FCS), multivariate normal (MVN), conditional quantile imputation (CQI) – on systematically missing data on gait speed in the Swedish National Study on Aging and Care (SNAC). Methods: In total, 1 000 IPDMA were simulated with four prospective cohort studies based on the characteristics of the SNAC. The three multiple imputation strategies were analysed with a two-stage common-effect multivariable logistic model targeting the effect of three levels of gait speed (100% missing in one study) on 5-years mortality with common odds ratios set to OR1 = 0.55 (0.8-1.2 vs ≤0.8 m/s), and OR2 = 0.29 (>1.2 vs ≤0.8 m/s). Results: The average combined estimate for the mortality odds ratio OR1 (relative bias %) were 0.58 (8.2%), 0.58 (7.5%), and 0.55 (0.7%) for the FCS, MVN, and CQI, respectively. The average combined estimate for the mortality odds ratio OR2 (relative bias %) were 0.30 (2.5%), 0.33 (10.0%), and 0.29 (0.9%) for the FCS, MVN, and CQI respectively. Conclusions: In our simulations of an IPDMA based on the SNAC where gait speed data was systematically missing in one study, all three imputation methods performed relatively well. The smallest bias was found for the CQI approach.
KW - gait speed
KW - individual participant data
KW - meta-analysis
KW - simulation
KW - systematically missing values
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U2 - 10.18632/aging.205552
DO - 10.18632/aging.205552
M3 - Article
C2 - 38358907
AN - SCOPUS:85186955259
SN - 0002-0966
VL - 16
SP - 3056
EP - 3067
JO - Aging
JF - Aging
IS - 4
ER -