Classifying patients into groups according to longitudinal series of measurements (ie, trajectory classification) has become frequent in clinical research. Most classification models suppose an equal intra-group variance across groups. This assumption is sometimes inappropriate because measurements in diseased subjects are often more heterogeneous than in healthy ones. We developed a new classification model for trajectories that uses unequal intra-group variance across groups and evaluated its impact on classification using simulations and a clinical study. The classification and typical trajectories were estimated using the classification Expectation Maximization (EM) algorithm to maximize the classification likelihood, the log-likelihood being profiled during the Maximization (M) step of the algorithm. The simulations showed that assuming equal intra-group variance resulted in a high misclassification rate (up to 50%) when the real intra-group variances were different. This rate was greatly reduced by allowing intra-group variances to be different. Similar classification was obtained when the real intra-group variances were equal, except when the total sample size and the number of repeated measurements were small. In a randomized trial that compared the effect of low vs standard cyclosporine A dose on creatinine levels after cardiac transplantation, the classification model with unequal intra-group variance led to more meaningful groups than with equal intra-group variance and showed distinct benefits of low dose. In conclusion, we recommend the use of a classification model for trajectories that allows for unequal intra-group variance across groups except when the number of repeated measurements and total sample size are small.
Keywords: ECM algorithm; classification; heterogeneity; intra-group variance; longitudinal measure; trajectories.
© 2018 John Wiley & Sons, Ltd.