Premium
Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients
Author(s) -
Desmée Solène,
Mentré France,
VeyratFollet Christine,
Sébastien Bernard,
Guedj Jérémie
Publication year - 2017
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12537
Subject(s) - prostate cancer , algorithm , computer science , nonlinear system , cancer , joint (building) , oncology , medicine , physics , architectural engineering , engineering , quantum mechanics
Summary Joint modeling is increasingly popular for investigating the relationship between longitudinal and time‐to‐event data. However, numerical complexity often restricts this approach to linear models for the longitudinal part. Here, we use a novel development of the Stochastic‐Approximation Expectation Maximization algorithm that allows joint models defined by nonlinear mixed‐effect models. In the context of chemotherapy in metastatic prostate cancer, we show that a variety of patterns for the Prostate Specific Antigen (PSA) kinetics can be captured by using a mechanistic model defined by nonlinear ordinary differential equations. The use of a mechanistic model predicts that biological quantities that cannot be observed, such as treatment‐sensitive and treatment‐resistant cells, may have a larger impact than PSA value on survival. This suggests that mechanistic joint models could constitute a relevant approach to evaluate the efficacy of treatment and to improve the prediction of survival in patients.