Novel statistical models and estimation methods for survival and longitudinal data.

生存和纵向数据的新颖统计模型和估计方法。

• 批准号: 217431-2013
• 负责人: Peng, Yingwei
• 金额: $ 1.09万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635922
• 关键词: Novel; statistical; models; estimation; methods

The objective of this research project is to investigate some important issues in analyzing survival data in clinical and observational studies and to propose novel statistical models and estimation methods to tackle the issues. One such issue is survival data with a cured fraction, where some subjects are immune to the event of interest and often named as cured. Such data can often be found in cancer treatments and in other disciplines such as economics and social studies. Another issue we will deal with is clustered survival data, where the subjects in the data can form clusters based on, for example, families or hospitals/medical centers. The survival time and cure status from the same cluster tend to be correlated due to shared genetics and environment. The issues also include handling survival data with a cured fraction from a case-cohort design, causal inference in survival models, and joint analysis of longitudinal proportional or multi-item data and survival data with a cured fraction. We will tackle the issues by developing new statistical models and estimation methods to properly analyze the data arising in the settings. For example, we will develop new estimating equations for the marginal cure models. The estimates from this new method are expected to be more efficient and less reliant on parametric assumptions than existing methods. We will develop data driven methods to select an appropriate model for survival data among a number of recently proposed cure models and classic survival models. We will develop cure models that are suitable for survival data in the case-cohort design. We will investigate the performance of some existing methods for causal inference and propose new methods that will be proved to be effective. The proposed research will contribute to advances in statistical methodology that has wide applications in real world problems in clinical and observational studies and in other disciplines. It will also provide opportunities to train even more Master's and PhD students and post-doctoral fellows than I achieved in the last NSERC proposal. It allows the trainees to develop skills and knowledge required as professional statisticians to deal with survival data.

该研究项目的目标是调查临床和观察性研究中生存数据分析中的一些重要问题，并提出新的统计模型和估计方法来解决这些问题。一个这样的问题是具有治愈分数的生存数据，其中一些受试者对感兴趣的事件具有免疫力，并且通常被命名为治愈。这些数据通常可以在癌症治疗和其他学科，如经济学和社会研究中找到。我们将处理的另一个问题是聚类生存数据，其中数据中的受试者可以基于例如家庭或医院/医疗中心形成聚类。由于相同的遗传和环境，来自同一聚类的存活时间和治愈状态往往相关。这些问题还包括处理来自病例队列设计的治愈分数的生存数据，生存模型中的因果推断，以及纵向比例或多项数据和治愈分数的生存数据的联合分析。我们将通过开发新的统计模型和估计方法来解决这些问题，以正确分析在环境中产生的数据。例如，我们将开发新的估计方程的边际治愈模型。预计这种新方法的估计比现有方法更有效，对参数假设的依赖程度更低。我们将开发数据驱动的方法来选择一个合适的模型中的一些最近提出的治愈模型和经典的生存模型的生存数据。我们将开发适合病例队列设计中生存数据的治愈模型。我们将研究一些现有的因果推理方法的性能，并提出新的方法，将被证明是有效的。拟议的研究将有助于统计方法的进步，在临床和观察研究以及其他学科的真实的世界问题中具有广泛的应用。它还将提供机会，培养更多的硕士和博士生和博士后研究员比我在上一次NSERC提案中实现的。它使受训者能够发展专业统计人员处理生存数据所需的技能和知识。