Applying machine learning-based multiple imputation methods to nonparametric multiple comparisons in longitudinal clinical studies

Dependent samples, in which repeated measurements are made on the same subjects, eliminate potential differences among the subjects. In k-dependent samples, missing data can occur for various reasons. The Skillings-Mack test is used instead of the Friedman test for k-dependent samples with missing observations that are non-normally distributed. If a significant difference exists among groups, nonparametric multiple comparisons need to be performed. In this study, we propose an innovative approach by applying four methods to nonparametric multiple comparisons of incomplete k-dependent samples that are non-normally distributed. The four methods are two nonparametric multiple imputation methods based on machine learning (multiple imputations by chained equations utilizing classification and regression trees (MICE-CART) and random forest (MICE-RF)), one nonparametric imputation method (random hot deck imputation), and the listwise deletion method. We compare the four methods under two missing data mechanisms, four correlation coefficients, two sample sizes, and three percentages of missingness. After implementing different scenarios in a simulation study, the listwise deletion method is inferior to the other methods. MICE-CART and MICE-RF are superior to the other methods for moderate and small sample sizes with well-controlled type 1 error. The two nonparametric multiple imputation methods based on machine learning can be applied to nonparametric multiple comparisons. Therefore, we propose machine learning-based multiple imputation methods for nonparametric multiple comparisons of k-dependent samples with missing observations. The approach was also illustrated with a longitudinal dentistry clinical trial.