Analysis and Modeling of Functional Data in Biostatistics

生物统计学中功能数据的分析和建模

• 批准号: RGPIN-2021-02963
• 负责人: Shi, Haolun
• 金额: $ 1.31万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765788
• 关键词: Analysis; Modeling; Functional; Data; Biostatistics

The long-term goal of my new research program is to develop useful computational tools and new statistical methodologies for modeling and analysis of complex functional and longitudinal data in various areas in biostatistics, such as medical imaging, survival analysis, and clinical trial design.  Over the next 5 years, my team will address three short-term objectives in three areas of application. Area 1: Novel methodological development of functional data analysis for biostatistics application. Functional data has wide a presence in biostatistics, in particular in the field of medical imaging and cancer biomarkers analysis. For example, conventional methods for functional data analysis on images of brain scans may incur high costs in terms of time and machine memory. Moreover, in cancer biomarkers analysis, trajectories are often subject to perturbation and missingness, and conventional methods for functional principal component analysis might not work effectively. To circumvent such computational difficulties, we will use a regression-based orthogonal approximation method to develop novel approaches for conducting functional principal component analysis. Area 2: Combination of functional data analysis for survival models. Existing survival models involving functional covariates typically rely on the Cox proportional hazards structure and the assumption of right censorship. Motivated by the aim of predicting the time of conversion to Alzheimer's disease from biomarker trajectories, we will combine the framework of functional regression and a generalized mixture cure model to develop several classes of functional survival models. The models will be versatile and able to handle both functional and scalar covariates, various types of censoring schemes, and both dense and sparse sampling structure. Area 3: Utilization of functional data information in trial design. This is particularly relevant for novel statistical trial designs for pandemic diseases such as the current COVID-19 pandemic. The efficacy endpoints for this type of disease is typically longitudinally ordinal. For each patient, these ordinal outcomes are observed daily across the entire timespan, forming a series of functional data objects. We will apply the methodologies of Bayesian adaptive sequential method to develop novel statistical designs for extracting information from the longitudinal data, which would enable efficient and effective testing and identification of useful treatment strategies. Taken together, the results of the proposed objectives will be of interest to researchers and developers in fields such as biostatistics and pharmaceutical statistics, in which the analysis of complex functional and longitudinal data is required. Ultimately, the analyses enabled by the developed statistical methodologies will bring new technologies that will benefit the health and well-being of Canadians and elevate Canada's reputation for statistical research on the world-stage.

我的新研究项目的长期目标是开发有用的计算工具和新的统计方法，用于对生物统计学各个领域的复杂功能和纵向数据进行建模和分析，例如医学成像、生存分析和临床试验设计。  在接下来的 5 年里，我的团队将在三个应用领域实现三个短期目标。领域 1：生物统计应用功能数据分析的新方法开发。功能数据在生物统计学中广泛存在，特别是在医学成像和癌症生物标志物分析领域。例如，对大脑扫描图像进行功能数据分析的传统方法可能会在时间和机器内存方面产生高昂的成本。此外，在癌症生物标志物分析中，轨迹经常受到扰动和缺失，并且传统的功能主成分分析方法可能无法有效发挥作用。为了规避这种计算困难，我们将使用基于回归的正交近似方法来开发进行函数主成分分析的新方法。区域 2：生存模型的功能数据分析组合。涉及功能协变量的现有生存模型通常依赖于 Cox 比例风险结构和正确审查的假设。出于从生物标志物轨迹预测转化为阿尔茨海默病的时间的目的，我们将结合功能回归框架和广义混合治愈模型来开发几类功能生存模型。这些模型将是通用的，能够处理函数和标量协变量、各种类型的审查方案以及密集和稀疏采样结构。 领域 3：在试验设计中使用功能数据信息。这对于流行病（例如当前的 COVID-19 大流行）的新颖统计试验设计尤其重要。此类疾病的疗效终点通常是纵向顺序的。对于每位患者，在整个时间跨度内每天观察这些顺序结果，形成一系列功能数据对象。我们将应用贝叶斯自适应顺序方法来开发新颖的统计设计，用于从纵向数据中提取信息，这将能够高效且有效地测试和识别有用的治疗策略。总而言之，所提出的目标的结果将引起生物统计学和药物统计学等领域的研究人员和开发人员的兴趣，这些领域需要对复杂的功能和纵向数据进行分析。最终，由已开发的统计方法进行的分析将带来新技术，这将有利于加拿大人的健康和福祉，并提升加拿大在世界舞台上统计研究的声誉。