The statistical performance of different strategies for dealing with missing values in clinical trials
ZHAO Shuzhen1, JIN Dongzhen1, LI Huihui1, LAI Mengyuan1, HUANG Ruogu1, MAO Guangyun1, 2.
1.Department of Preventive Medicine, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China; 2.Center on Clinical Research, the Eye Hospital of Wenzhou Medical University,Wenzhou 325027, China
ZHAO Shuzhen,JIN Dongzhen,LI Huihui, et al. The statistical performance of different strategies for dealing with missing values in clinical trials[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2022, 52(8): 632-637.
Abstract：Objective: To evaluate the statistical performance of various missing value processing methods in a two-arm superiority clinical trial with different missing patterns, mechanisms and proportions. Methods:We generated longitudinal simulation datasets containing monotonic and arbitrary missing patterns, missing mechanisms that occurred at random or completely at random, and missing proportions of 0%-5%, 5%-10% and 10%-15%, respectively. Estimations of efficacy between the two groups after performing treatment in each simulated dataset by different missing value handling methods, and the statistical performance of the different methods were evaluated according to the magnitude of the difference in estimated efficacy from the full data set.Results: When the proportion of missingness was <5%, the effect estimates obtained from different missingness treatments in any missing patterns were close to each other, and the effect estimates from repeated measures mixed effects model (MMRM) and the analysis of covariance with multiple imputations in monotonic missing patterns were closest to the true values. When the proportion of missingness >5%, the effect estimates from MMRM with different covariance matrix structures and the analysis of covariance with multiple imputations were still closest to the true values regardless of the missing mechanisms and missing patterns, with the former more stable than the latter. In contrast, the error in the effect estimates of the single imputation approach and the mixed-mode model (PMM) increased with the proportion of missing, especially for the monotonic missing, where the error was greatest when the proportion of missing was 10%-15%. Conclusion: MMRM yielded the most accurate effect estimates for different missing proportions (0%-5%, 5%-10% and 10%-15%), missing patterns (monotonic missing or arbitrary missing), and missing mechanisms (completely random missing or random missing),suggesting that MMRM is the preferred solution for dealing with missing longitudinal quantitative data in twoarm superiority clinical trials.
