Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints

• Published in: Statistics in medicine 2023-09, Vol.42 (22), p.3996-4014
• Main Authors: Proust‐Lima, Cécile, Saulnier, Tiphaine, Philipps, Viviane, Traon, Anne Pavy‐Le, Péran, Patrice, Rascol, Olivier, Meissner, Wassilios G., Foubert‐Samier, Alexandra
• Format: Article
• Language: English
• Subjects: Atrophy; Biomarkers; Brain diseases; class‐membership; Clinical outcomes; clustering; Disease; Genotype & phenotype; heterogeneity; joint modeling; Latent class analysis; Life Sciences; Medical prognosis; multiple system atrophy; multivariate longitudinal data; Neurodegeneration; neurodegenerative diseases; Pathology; Santé publique et épidémiologie
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.9844

Summary Neurodegenerative diseases are characterized by numerous markers of progression and clinical endpoints. For instance, multiple system atrophy (MSA), a rare neurodegenerative synucleinopathy, is characterized by various combinations of progressive autonomic failure and motor dysfunction, and a very poor prognosis. 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 a highly suspected heterogeneity between patients. Yet, such description is crucial for understanding the natural history of the disease, staging patients diagnosed with the disease, unravelling subphenotypes, and predicting the prognosis. Through the example of MSA progression, we show how a latent class approach modeling multiple repeated markers and clinical endpoints can help describe complex disease progression and identify subphenotypes for exploring new pathological hypotheses. The proposed joint latent class model includes class‐specific multivariate mixed models to handle multivariate repeated biomarkers possibly summarized into latent dimensions and class‐and‐cause‐specific proportional hazard models to handle time‐to‐event data. Maximum likelihood estimation procedure, validated through simulations is available in the lcmm R package. In the French MSA cohort comprising data of 598 patients during up to 13 years, five subphenotypes of MSA were identified that differ by the sequence and shape of biomarkers degradation, and the associated risk of death. In posterior analyses, the five subphenotypes were used to explore the association between clinical progression and external imaging and fluid biomarkers, while properly accounting for the uncertainty in the subphenotypes membership.