Statistical Methods for Complex Life History Studies

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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. **