Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients.

Autor: Desmée S; INSERM, IAME, UMR 1137, F-75018 Paris, France.; Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, F-75018 Paris, France., Mentré F; INSERM, IAME, UMR 1137, F-75018 Paris, France.; Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, F-75018 Paris, France., Veyrat-Follet C; Drug Disposition, Disposition Safety and Animal Research Department, Sanofi, Alfortville, France., Sébastien B; Biostatistics and Programming, Sanofi, Chilly-Mazarin, France., Guedj J; INSERM, IAME, UMR 1137, F-75018 Paris, France.; Université Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, F-75018 Paris, France.
Jazyk: angličtina
Zdroj: Biometrics [Biometrics] 2017 Mar; Vol. 73 (1), pp. 305-312. Date of Electronic Publication: 2016 May 05.
DOI: 10.1111/biom.12537
Abstrakt: 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.
(© 2016, The International Biometric Society.)
Databáze: MEDLINE
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