Hierarchical Nonlinear Mixed Effect Modeling: Defining Post-Radiation Therapy Relapse in Prostate Cancer Patients.

摘要

During this grant period, a linear-quadratic random effects model of the profile of PSA levels in men following radiation therapy for prostate cancer was developed. It can be used to predict, for a new patient, the expected trajectory of future PSA levels, and the probability of biochemical failure. The model, which includes terms for initial PSA, post-treatment PSA nadir, time of nadir, and future PSA level, also allows us to update these predictions as new PSA measurements on the patient are collected. We show that our method has some advantages over the widely used definition of biochemical failure as three consecutive rises in post-nadir PSA level.