Flexible statistical models for dynamic prediction in survival analysis with time-varying prognostic factors

我的研究的长期目标是开发新的，灵活的统计方法，这将提高对许多疾病的发生，进展，治疗和结果的临床研究的科学有效性。人类健康的研究面临着许多概念和方法上的挑战，生物统计学的使命是制定复杂的方法来应对这些挑战。在接下来的5年里，我的研究将集中在改进现有的统计方法，以处理与以下方面相关的复杂性：a）风险和预后因素随时间的变化，以及它们对健康结果的影响，和B）个体患者在疾病演变过程中可能经历的众多间歇性和最终临床结果。在临床实践中，患者及其医生必须了解生活方式、饮食和其他可改变的风险因素以及疾病进展的实验室检查和生物标志物随时间的变化如何影响住院、受伤、疾病复发或死亡的风险。通过分析动态纵向过程的几个方面，解开各种患者特征、生物标志物和治疗的影响是复杂的。首先，虽然同时影响给定临床终点风险的许多因素彼此相关。另一个挑战涉及风险因素变化与治疗之间的复杂时间关系，包括过去治疗或风险因素值的延迟或累积效应，这些效应也可能相互影响。例如，尽管抗高血压治疗会影响患者当前的血压，但是开处方、停止或改变这种治疗的剂量的决定又可以取决于患者先前的血压及其最近的变化。此外，最终结果（如中风）可能会受到血压史以及治疗的其他影响的影响，包括治疗的间接益处和可能的非预期“副作用”（不良反应）。为了应对这些挑战，我的研究将建立在统计理论和计算技术的最新进展，以及我过去在开发新方法分析健康结果纵向研究方面的经验。通过我的大量临床合作，拟议研究的结果将有助于改善许多疾病的临床预后和治疗。

The long-term objective of my research is to develop new, flexible statistical methods that will enhance the scientific validity of clinical studies on the occurrence, progression, treatment and outcomes of many diseases. Studies of human health face many conceptual and methodological challenges, and the mission of biostatistics is to elaborate sophisticated methods to address these challenges.******In the next 5 years, my research will focus on improving the existing statistical methods to handle complexities related to a) changes over time in risk and prognostic factors, as well as in their impact on the health outcomes, and b) multitude of intermittent and final clinical outcomes individual patients may experience during the evolution of their disease. In clinical practice, patients and their physicians have to understand how changes over time, in lifestyle, diet and other modifiable risk factors, as well as in the laboratory tests and biomarkers of disease progression, may affect the risk of hospitalizations, injuries, disease recurrence, or death. Disentangling the impact of various patient characteristics, biomarkers and treatments is complicated by several aspects of dynamic, longitudinal processes being analyzed. First, while many factors that affect simultaneously the risk of a given clinical endpoint, are correlated with each other. Another challenge is related to complex temporal relationships between changes in risk factors and treatments, including delayed or cumulative effects of past treatments or risk factor values, which may also affect each other. For example, whereas an anti-hypertensive treatment will affect the patient's current blood pressure, the decision to prescribe, discontinue or change the dose of such treatment may, in turn, depend on the patient's previous blood pressure and its recent changes. In addition, the final outcome (such as a stroke) may be affected by blood pressure history, as well as by the other effects of the treatment, including both indirect benefits and possible unintended “side effects” (adverse reactions) of the treatment.******To address these challenges, my research will build on the recent progresses in both statistical theory and computational techniques, as well as on my past experience in developing new methods for analyzing longitudinal studies of health outcomes.******Through my numerous clinical collaborations, the results of the proposed research will help improve clinical prognosis and treatment of many diseases.**