A New Class of Mechanistic Risk Prediction Models for Cancer Treatment Outcomes

描述(申请人提供)：这项研究的总体目标是开发一个统计框架，为建立一个智能临床支持系统奠定基础，该系统可以预测前列腺癌治疗的不同情景下的潜在结果。在这项建议中，我们建议将统计学方法与癌症治疗机制相结合，通过有效地考虑治疗特征和治疗后个体生物标志物的信息，开发一类新的统计回归模型来预测肿瘤复发的概率和时间。新的模型是从一个迭代的细胞出生和死亡过程中衍生出来的，模拟了治疗后肿瘤细胞的生物学机制，从而在统计模型建立和治疗结果预测中调用了生物学因素。从统计学上讲，新模型既考虑了比例风险结构，也考虑了非比例风险结构，纳入了治愈率，并考虑了短期癌症复发预防和长期生化无病生存的非均匀治疗效果。该模型通过允许对单个协变量的更一般的依赖来扩展治愈率模型，并且具有半参数性质：非参数分量涉及癌症进展时间分布，而参数分量涉及治疗特征、治疗后生物标记物和其他重要协变量。我们提出了估计级数时间分布的非参数平滑方法，以及参数估计的似然和贝叶斯方法。该方法将应用于罗切斯特大学医学中心收集的前列腺癌患者的临床随访数据。该项目的创新之处在于，新模型本质上是基于肿瘤治疗反应的生物学机制，并利用统计建模技术的力量进行风险预测。在这项R21应用中，我们的目标是使用统计技术开发新模型，如果成功，将在未来提交R01提案，以全面开发预测模型，并应用构建和验证预测计算机支持系统，以帮助医生做出明智的临床决策，以适应癌症治疗策略。受治疗后肿瘤发展的随机建模的启发，该项目建议开发一类新的统计回归模型来预测前列腺癌复发的时间相关风险。

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.