Statistical Methods for Complex Life History Studies

拟议研究的广泛目标是为复杂过程和数据的设计和分析开发新的方法。在医学、公共卫生、遗传学和其他不断产生新型研究的领域的应用推动了将要讨论的具体主题。此外，除了与实验或观察性研究有关的较小数据集之外，由于有了非常大的数据库，正在改变研究的某些方面。******需要创新的统计方法来处理与健康、教育、就业和人类寿命的其他方面有关的生活史进程。这些过程的复杂性，影响它们的因素，以及收集代表性个人群体详细数据的困难，往往使他们的研究变得困难，而有偏见或不完整的数据，如果处理不当，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。使用大型观测数据库（或“大”数据）进行科学发现和推理也需要调查。这类数据库经常缺少关键变量，并且存在其他数据质量问题，限制了仅基于它们的推理。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。******建议在三个主要领域进行研究：(i)设计和分析生活史研究的方法，（ii）整合来自多个数据源的信息的方法，以及（iii）设计和分析两阶段或多阶段研究。该研究将为研究复杂生命史过程和使用多种数据源了解复杂现象提供重要的统计工具。与多伦多大学（如Laurent Briollais、Shelley Bull、孙磊）和其他地方（如纽芬兰纪念大学的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发展，与Richard Cook和Peisong Han（滑铁卢大学）的合作将加强在生命历史分析和大数据利用方面的工作。**

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. **