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- Title
Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients.
- Authors
Desmée, Solène; Mentré, France; Veyrat‐Follet, Christine; Sébastien, Bernard; Guedj, Jérémie
- Abstract
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.
- Subjects
PROSTATE-specific antigen; STOCHASTIC approximation; SURVIVAL analysis (Biometry); LINEAR statistical models; TREATMENT effectiveness
- Publication
Biometrics, 2017, Vol 73, Issue 1, p305
- ISSN
0006-341X
- Publication type
Article
- DOI
10.1111/biom.12537