Modeling Clinical Failure in Prostate Cancer Patients Based on a Two-stage Statistical Model

Biomarker series can indicate disease progression and predict clinical endpoints. When a treatment is prescribed depending on the biomarker, confounding by indication might be introduced if the treatment modifies the marker profile and risk of failure.

The two-stage model fitted within a Bayesian Markov Chain Monte Carlo framework is particularly flexible to account for such data. Prostate-specific antigens in prostate cancer patients treated with external beam radiation therapy can be monitored. In the presence of rising prostate-specific antigens after external beam radiation therapy, salvage hormone therapy can be prescribed to reduce both the prostate-specific antigens concentration and the risk of clinical failure, an illustration of confounding by indication. The prognostic value of hormone therapy and prostate-specific antigens trajectory on the risk of failure based on a two-stage model within a Bayesian framework to assess the role of the prostate-specific antigens profile on clinical failure while accounting for a secondary treatment prescribed by indication. the aim of this research is to model prostate specific antigens using a hierarchical piecewise linear trajectory with a random changepoint. Residual prostate-specific antigens variability can be expressed as a function of prostate-specific antigens concentration. Covariates in the survival model can include : hormone therapy, baseline characteristics, and individual predictions of the prostate-specific antigens nadir and timing and prostate-specific antigens slopes before and after the nadir as provided by the longitudinal process.