||5R21CA131603-02 Interpret this number
||University Of Rochester
||A New Class of Mechanistic Risk Prediction Models for Cancer Treatment Outcomes
DESCRIPTION (provided by applicant): The general objective of this study is to develop a statistical framework to lay a foundation for building an intelligent clinical support system with predictions of potential outcomes under different scenarios of prostate cancer treatment. In this proposal, combining statistical methods with cancer treatment mechanism, we propose to develop a new class of statistical regression models for predicting the probability and the timing of tumor recurrence by effectively taking account of information on treatment characteristics and post-treatment individual biomarkers. The new model is derived from an iterated cell birth and death process, mimicking the biological mechanism of tumor cells after treatments, and thereby invokes biological considerations in statistical model building and treatment outcome prediction. Statistically speaking, the new model allows for both proportional and non-proportional hazards structures, incorporates a cure rate, and accommodates non-homogeneous treatment effects on short-term cancer recurrence prevention and long-term biochemical disease-free survival. The proposed model extends the cure rate models by allowing for a more general dependence on individual covariates, and it is of semiparametric nature: the nonparametric component involves the cancer progression time distribution and the parametric component involves treatment characteristics, post-treatment biomarkers, and other significant covariates. We propose nonparametric smoothing techniques for estimation of the progression time distribution, and likelihood and Bayesian methods for parametric estimation. The methodology will be applied to clinical follow-up data of prostate cancer patients amassed at The University of Rochester Medical Center. The novelty of this project is that the new model is essentially based on biological mechanism of tumor response to treatment and utilizes strength of statistical modeling techniques for risk prediction. In this R21 application, we aim to develop the new model using statistical techniques, and if successful, an R01 proposal will be submitted in the future to fully develop the prediction model with application to construct and validate a prediction computer support system to assist physicians in making informed clinical decisions for adaptive cancer treatment strategies. Motivated by stochastic modeling of post-treatment tumor development, the project proposes to develop a new class of statistical regression models for predicting the time- dependent risk of prostate cancer recurrence.