Statistical Methods for Complex Life History Studies

复杂生活史研究的统计方法

• 批准号: RGPIN-2017-04055
• 负责人: Lawless, Jerald
• 金额: $ 2.49万
• 依托单位: University of Waterloo
• 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=765767
• 关键词: Statistical; Methods; Complex; Life; History

The broad objectives of the proposed research are to develop new methodology for the design and analysis of complex processes and data. The specific topics to be addressed are motivated by applications in medicine, public health, genetics and other areas where new types of studies are constantly being created. In addition, some aspects of research are being transformed through the availability of very large data bases, in addition to the smaller data sets associated with experimental or observational studies.Innovative statistical methods are needed to deal with life history processes associated with health, education, employment and other aspects of human lifetimes. The complexity of such processes, the factors affecting them, and the difficulty in collecting detailed data on representative groups of individuals often makes their study difficult, and biased or incomplete data, if not handled appropriately, can result in misleading inferences. Conflicting claims and conclusions concerning medical interventions or the effects of lifestyle or environmental exposures on health are, for example, often due to such factors. The use of large observational data bases (or “big” data) for scientific discovery and inference also require investigation. Such data bases are often missing key variables and suffer from other data quality problems that limit inference based on them alone. By integrating information from different sources, however, we may be able to reach more reliable conclusions.Research is proposed in three main areas: (i) methods for the design and analysis of life history studies, (ii) methods for integrating information from multiple data sources, and (iii) the design and analysis of two- or multi-phase studies. This research will add significantly to the statistical tools for studying complex life history processes, and for using multiple data sources to learn about complex phenomena. Collaborations with researchers in medicine, public health and genetic epidemiology at the University of Toronto (e.g. Laurent Briollais, Shelley Bull, Lei Sun) and elsewhere (e.g. Yildiz Yilmaz, Memorial University of Newfoundland) will motivate aspects of the research related to disease and health, and collaborations with Richard Cook and with Peisong Han (Waterloo) will enhance the work on life history analysis and on the utilization of big data.

拟议研究的广泛目标是开发设计和分析复杂过程和数据的新方法。要解决的具体主题受到医学、公共卫生、遗传学和其他不断创造新类型研究的领域的应用的推动。此外，除了与实验或观察性研究相关的较小数据集外，研究的某些方面正在通过提供非常大的数据库而发生变化。需要创新的统计方法来处理与健康、教育、就业和人类生活的其他方面有关的生活史过程。这种过程的复杂性、影响因素以及收集有代表性的个人群体的详细数据的困难，往往使他们的研究变得困难，如果处理不当，有偏见或不完整的数据可能导致误导性的推论。例如，关于医疗干预或生活方式或环境暴露对健康的影响的相互矛盾的主张和结论往往是由于这些因素造成的。利用大型观测数据库(或“大数据”)进行科学发现和推理也需要调查。这类数据库往往缺少关键变量，并受到其他数据质量问题的影响，这些问题限制了仅基于它们的推断。然而，通过整合来自不同来源的信息，我们可能能够得出更可靠的结论。研究提出了三个主要领域：(I)生活史研究的设计和分析方法，(Ii)整合来自多个数据来源的信息的方法，以及(Iii)两阶段或多阶段研究的设计和分析。这项研究将大大增加研究复杂生活史过程的统计工具，并使用多个数据来源来了解复杂现象。与多伦多大学(例如Laurent Briollais、Shelley Bull、Lei Sun)和其他地方(例如纽芬兰纪念大学Yildiz Yilmaz)的医学、公共卫生和遗传流行病学研究人员的合作将推动与疾病和健康相关的研究方面的研究，与理查德·库克和韩培松(滑铁卢)的合作将加强生活史分析和大数据利用方面的工作。