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

用于随时间变化的预后因素进行生存分析的动态预测的灵活统计模型

• 批准号: RGPIN-2016-04424
• 负责人: Abrahamowicz, Michal
• 金额: $ 1.6万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615054
• 关键词: Flexible; statistical; models; dynamic; prediction

The long-term objective of my research is to develop new 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 clinical 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 treatments, 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 factors, as well as in the type, dosage and duration of treatment, may affect the risk of hospitalizations, injuries, adverse drug reactions, disease recurrence, or death. Disentangling the impact of various patient characteristics and treatments is complicated by several aspects of dynamic, longitudinal processes being analyzed. First, while many factors affect simultaneously the risk of a given clinical endpoint, individual factors are related 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 program will help improve clinical prognosis and treatment of many diseases.

我研究的长期目标是开发新的统计方法，以提高许多疾病的发生，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，临床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）风险和预后因素以及治疗随时间的变化，以及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局。在临床实践中，患者和他们的医生必须了解随着时间的推移，生活方式，饮食和其他可改变的因素，以及治疗的类型，剂量和持续时间的变化如何影响住院，受伤，药物不良反应，疾病复发或死亡的风险。通过分析动态的纵向过程的几个方面，解开各种患者特征和治疗的影响是复杂的。首先，虽然许多因素同时影响给定临床终点的风险，但个体因素彼此相关。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的纵向研究的经验。 通过我的大量临床合作，拟议研究计划的结果将有助于改善许多疾病的临床预后和治疗。