Statistical Methods in the Design and Analysis of Clinical Trials and Assessment of Prediction with Patient-Specific Information

在接下来的五年里，我将开发新的方法来估计，测试和进行统计推断，以提高基于患者特定数据的预测模型的准确性。将研究各种删失的生存结局数据的预测误差，适用于治愈和未治愈患者人群的治愈率模型以及高维数据。这项研究对于健康和医学研究人员来说非常重要，他们正在预测患者的体质，疾病类别或对治疗的反应。它也是统计学方法研究的一个重要领域，需要计算方法和理论发展。我要培养一个博士。学生在处理这些统计挑战，建立在这方面的研究，为实际应用和统计科学的进步。 当治疗效果随患者生物标志物测量值而变化时，我还将开发用于临床试验设计和分析的新方法。一种方法是假设对不同生物标志物阈值定义的患者子集的额外治疗效果。另一种方法是非参数的，允许治疗效果随生物标志物而变化，而无需预先指定的函数形式。阈值模型方法因其清晰的实用解释而对临床研究者非常有用。我将指导两名硕士生为临床研究者建立样本量设计指南。我要培养一个博士。学生对各种阈值模型进行理论和方法研究。第二种方法更一般，它以灵活的形式提供了治疗效果的直接视图。我会再培养一个博士学生开发各种类型数据的非参数模型。这一大方向的计划工作旨在通过生物标志物研究改善临床试验，并为加拿大的统计研究增添新的前沿。 它解决了靶向治疗和个性化医疗临床试验研究中出现的紧迫统计问题。

In the next five years, I will develop new methods in estimation, testing and making statistical inference for the accuracy of prediction models built upon patient specific data. Prediction error will be studied for survival outcome data with various types of censoring, for cure-rate models which accommodate cured and uncured patient populations and for high dimensional data. The research is important for health and medical researchers who are making predictions for patients' prognostics, disease categories or responses to therapy. It is also an important area of methodological research in statistics in need of computational methods and theoretical development. I will train a Ph.D. student in dealing with these statistical challenges to build up the research in this area for both practical use and advancement of statistical science. I will also develop new methods for clinical trial design and analysis when the treatment effect varies with patients' biomarker measurements. One approach is to assume additional treatment effects on patient subsets defined on different biomarker thresholds. The other approach is nonparametric and allows the treatment effect to change with the biomarker without a pre-specified functional form. The threshold model approach is very useful for clinical researchers for its clear practical interpretation. I will supervise two M.Sc. students in building sample size designing guidelines for clinical researchers. I will train one Ph.D. student on theoretical and methodological study on various threshold models. The second approach is more general, and it provides a direct view of the treatment effect in a flexible form. I will train another Ph.D. student on developing the nonparametric models for various types of data. The planned work in this general direction aims to improve clinical trials through biomarker study, and adds a new frontier to the statistical research in Canada. It addresses the urgent statistical issues arising from clinical trial study on targeted treatments and personalized medicine.