Abstract
BackgroundIn many clinical applications, evolution of a longitudinal marker is censored by an event occurrence, and, symmetrically, event occurrence can be influenced by the longitudinal marker evolution. In such frameworks joint modeling is of high interest. The Joint Latent Class Model (JLCM) allows to stratify the population into groups (classes) of patients that are homogeneous both with respect to the evolution of a longitudinal marker and to the occurrence of an event; this model is widely employed in real-life applications. However, the finite sample-size properties of this model remain poorly explored.MethodsIn the present paper, a simulation study is carried out to assess the impact of the number of individuals, of the censoring rate and of the degree of class separation on the finite sample size properties of the JLCM. A real-life application from the neurology domain is also presented. This study assesses the precision of class membership prediction and the impact of covariates omission on the model parameter estimates.ResultsSimulation study reveals some departures from normality of the model for survival sub-model parameters. The censoring rate and the number of individuals impact the relative bias of parameters, especially when the classes are weakly distinguished. In real-data application the observed heterogeneity on individual profiles in terms of a longitudinal marker evolution and of the event occurrence remains after adjusting to clinically relevant and available covariates;ConclusionThe JLCM properties have been evaluated. We have illustrated the discovery in practice and highlights the usefulness of the joint models with latent classes in this kind of data even with pre-specified factors. We made some recommendations for the use of this model and for future research.
Highlights
In many clinical applications, evolution of a longitudinal marker is censored by an event occurrence, and, symmetrically, event occurrence can be influenced by the longitudinal marker evolution
Joint latent class model The joint latent class model is composed of three submodels: a multinomial logistic regression defining the probability of belonging to a latent class, a mixed linear model for each latent class describing the evolution of the longitudinal marker, and a survival model accounting for the time-to-event for each class
The capacity of the model to distinguish between the latent classes is investigated by considering two different settings in terms of class separation: high separation and low separation
Summary
Evolution of a longitudinal marker is censored by an event occurrence, and, symmetrically, event occurrence can be influenced by the longitudinal marker evolution In such frameworks joint modeling is of high interest. The most popular way to analyze this kind of combined data are the shared random effects models, proposed by Wulfsohn and Tsiatis [2], where a function of random effects, issued from the model for longitudinal marker, is included as a covariate into the survival model This approach allows to explain the relation between a longitudinal parameter and a time-to-event, assuming a homogeneous population. The JLCM allows firstly to account for the dependency between a longitudinal biomarker and a time-to-event by distinguishing between different profiles of biomarker progression associated with the risk of event. The JLCM provides predictions for the risk of event conditional on the biomarker progression
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