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In metastatic castration-resistant prostate cancer (mCRPC) clinical trials,the assessment of treatment efficacy essentially relies on the time-to-deathand the kinetics of prostate-specific antigen (PSA). Joint modelling has beenincreasingly used to characterize the relationship between a time-to-event anda biomarker kinetics but numerical difficulties often limit this approach tolinear models. Here we evaluated by simulation the capability of a new featureof the Stochastic Approximation Expectation-Maximization algorithm in Monolixto estimate the parameters of a joint model where PSA kinetics was defined by amechanistic nonlinear mixed-effect model. The design of the study and theparameter values were inspired from one arm of a clinical trial. Increasinglyhigh levels of association between PSA and survival were considered and resultswere compared with those found using two simplified alternatives to jointmodel, a two-stage and a joint sequential model. We found that joint modelallowed for a precise estimation of all longitudinal and survival parameters.In particular the effect of PSA kinetics on survival could be preciselyestimated, regardless of the strength of the association. In contrast, bothsimplified approaches led to bias on longitudinal parameters and two-stagemodel systematically underestimated the effect of PSA kinetics on survival. Insummary we showed that joint model can be used to characterize the relationshipbetween a nonlinear kinetics and survival. This opens the way for the use ofmore complex and physiological models to improve treatment evaluation andprediction in oncology.

the aaps journal

Nonlinear Mixed-Effect Models for Prostate-Specific Antigen Kinetics and  Link with Survival in the Context of Metastatic Prostate Cancer: a Comparison  by Simulation of Two-Stage and Joint Approaches