Clustering of trajectories with mixed effects classification model: Inference taking into account classification uncertainties

Classifying patient biomarker trajectories into groups has become frequent in clinical research. Mixed effects classification models can be used to model the heterogeneity of longitudinal data. The estimated parameters of typical trajectories and the partition can be provided by the classification version of the expectation maximization algorithm, named CEM. However, the variance of the parameter estimates obtained underestimates the true variance because classification uncertainties are not taken into account. This article takes into account these uncertainties by using the stochastic EM algorithm (SEM), a stochastic version of the CEM algorithm, after convergence of the CEM algorithm. The simulations showed correct coverage probabilities of the 95% confidence intervals (close to 95% except for scenarios with high bias in typical trajectories). The method was applied on a trial, called low-cyclo, that compared the effects of low vs standard cyclosporine A doses on creatinine levels after cardiac transplantation. It identified groups of patients for whom low-dose cyclosporine may be relevant, but with high uncertainty on the dose-effect estimate.