B0885
Title: Describing complex disease progression with latent class models for multivariate longitudinal markers and times-to-event
Authors: Cecile Proust-Lima - INSERM (France) [presenting]
Tiphaine Saulnier - Univ Bordeaux - Inserm (France)
Viviane Philipps - Univ Bordeaux - Inserm (France)
Alexandra Foubert-Samier - Univ Bordeaux - Inserm (France)
Abstract: Some diseases are characterized by numerous markers of progression. Although not specific, this is mainly the case in neurodegenerative diseases where pathological brain changes may induce multiple clinical signs on which the progression of a patient is assessed. For instance, Multiple System Atrophy (MSA), a rare neurodegenerative synucleinopathy, is characterized by various combinations of progressive autonomic failure and motor dysfunction, and by a very poor prognosis with a median survival of a few years after diagnosis. Describing the progression of such complex and multi-dimensional diseases is particularly difficult. One has to simultaneously account for the assessment of multivariate markers over time, the occurrence of clinical endpoints, and the highly suspected heterogeneity between patients partly due to the difficulty of formally diagnosing the disease. Yet, such description is crucial for understanding the natural history of the disease, staging patients diagnosed with the disease, unraveling subphenotypes, and predicting the prognosis. Through the example of MSA progression, we show how a latent class approach, implemented in the R package lcmm, can help describe complex disease progression measured by multiple repeated markers and clinical endpoints, and identify subphenotypes for exploring new pathological hypotheses.
