Shelley Bull, SSC Award for Impact of Applied and Collaborative Work 2015
The 2015 recipient of the Statistical Society of Canada Award for Impact of Applied and Collaborative Work is Shelley Bull, Senior Investigator in the Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital and Professor in the Division of Biostatistics, Dalla Lana School of Public Health at the University of Toronto. The award recognizes outstanding contributions by members of the SSC in collaborative research and applied work, the importance of which derives primarily from its relatively recent impact on a subject area outside of the statistical sciences, on an area of application, or on an organization.
Born in Hamilton Ontario, Shelley grew up in Burlington and Etobicoke in southern Ontario. She obtained BMath (1976) and MMath (1977) degrees at the University of Waterloo where faculty members in the Department of Statistics and Actuarial Sciences fostered her interests in biostatistics and applied work. Following doctoral and post-doctoral work in the Department of Epidemiology and Biostatistics at the University of Western Ontario (1978-86), completed under the supervision and mentorship of Allan Donner, Shelley joined the Clinical Epidemiology Group in the newly-created Research Institute at Mount Sinai Hospital, Toronto and was appointed as Assistant Professor in the Department of Preventive Medicine and Biostatistics, University of Toronto. There she developed an independent research program focused on categorical data modeling in epidemiology, continued collaborations in population-based surveys of attitudes towards anti-smoking measures, and initiated new collaborations in the molecular genetics of breast cancer. In the mid-1990's, with the advent of new molecular technologies and international initiatives such as the Human Genome Project, Shelley began to work more intensively on problems in statistical genetics and molecular and genetic epidemiology, taking on leadership in the development of a multi-institution research group in Toronto which expanded into a national research team as part of the Network of Centres of Excellence in Mathematics (MITACS), and in the establishment of a University of Toronto interdisciplinary training program in Genetic Epidemiology and Statistical Genetics (CIHR STAGE, with F. Gagnon).
Her excellence as a statistical scientist working in epidemiology and biostatistics has been recognized by multiple research awards: doctoral and postdoctoral awards from the National Health Research Development Program (NHRDP 1979-83) and the Ontario Ministry of Health (1983-85) respectively, a National Health Research Scholar Award (1989-99), and a Canadian Institutes of Health Research (CIHR) Senior Investigator Award (2002-07). She is also a recipient of The Anthony Miller Award For Excellence in Research In Public Health, Graduate Department of Public Health Sciences, University of Toronto (2001), the Genetic Epidemiology Best Paper Award from the International Genetic Epidemiology Society (2002, with Darlington, Greenwood, Shin), and the International Genetic Epidemiology Society Leadership Award (2012).
Shelley has authored or co-authored over 180 peer-reviewed research papers and book chapters encompassing statistical methods and their applications in population and public health; in perinatology, nephrology and rheumatology; and in molecular cancer genetics, molecular and genetic epidemiology, human genetics, and statistical genetics. This work reflects the synergistic relationship between her basic biostatistical and applied research programs, typified by the concurrent application of innovative statistical methods to bear on substantive scientific questions, with the identification of gaps in statistical methodology and the need for new methods motivated by collaboration with colleagues from other disciplines and their understanding of current scientific approaches. Over the years, Shelley has made the training of graduate students and post-doctoral fellows in statistical research methods and interdisciplinary collaborative research increasingly integral to her research program. She credits her collaborations with trainees and colleagues, both statistical and non-statistical, as a wonderful source of intellectual stimulation that has contributed largely to her understanding of statistical, biomedical, and epidemiological science.
Her interest in methods for categorical data analysis, beginning with doctoral and post-doctoral work in large sample efficiency of multinomial logistic regression, found expression in an early application of GEE to multiple categorical outcomes in large observational studies and in extensions of affected sib pair genetic linkage methods to account for heterogeneity by use of covariates. Involving a number of graduate students and post-doctoral fellows, Shelley's group also developed new approaches for practical inference under a penalized multinomial likelihood particularly useful with sparse data. Their findings are relevant to genetic association analysis of low frequency and rare variants now being generated by next generation sequencing technologies and to the study of multiple traits with shared genetic determinants.
In a long-term collaboration with molecular geneticist
Dr. Andrulis of the Lunenfeld-Tanenbaum Research Institute, Shelley's work addressed the design and analysis of studies to investigate the role of molecular genetic alterations in tumour biology and risk of metastasis. Early investigations of selected DNA alterations in breast cancer tumours revealed molecular interactions and time-dependent associations with early metastasis-free survival in a large prospective cohort of patients. Subsequent challenges in analyses of unselected genomic markers, measured using microarrays that quantify RNA gene expression or DNA alterations, led post-doctoral statistical research fellows within the group to develop methods to effectively analyse and integrate high-dimensional tumour measurements. The establishment of tissue microarrays (TMAs) to measure tumour protein expression in the cohort further stimulated implementation of advanced statistical methods. In particular, application of mixture-cure models capable of modeling both early and long-term molecular associations with risk of metastasis led to novel insights and helped explain counter-intuitive results obtained by standard methods. She anticipates on-going work to focus increasingly on integration of multiple molecular features for understanding of biological mechanisms.
The development of statistical methods for the detection, localization and characterization of susceptibility genes associated with complex traits and diseases by means of genetic analysis of genotyping and sequencing data collected from extended pedigrees, small families and unrelated individuals is a major focus of Shelley's research program. Graduate students and post-doctoral fellows working with Shelley and colleagues have developed and evaluated complementary types of statistical genetic analysis including models that relate genetic similarity among relatives to functions of their trait values and other measurable characteristics, as well as methods to integrate data from parent-offspring trios and unrelated individuals, and models for regional/gene-based analysis and for multiple correlated traits in unrelated individuals. These methods were applied in genetic studies of inflammatory bowel disease, kidney stone disease, arthritis, hypertension, diabetes, blood pressure, and blood lipids. Motivated by the wide adoption of high-density genotyping technologies and associated concerns about the reliability of genetic effect detection and estimation in the face of high-dimensional multiple testing, Shelley and collaborators developed a general non-parametric bootstrap resampling method to address the upward bias in genetic effect estimation resulting from the application of stringent criteria for statistical significance to control false positive findings. Bias-reduced estimates are helpful in interpretation and in power considerations for replication study design. Resampling-based estimators are attractive because they require only specification of well-defined parameter estimation and hypothesis testing procedures, and are broadly useful for quantitative, binary and time-to-event traits. Recent work is exploring approaches to multi-phase, multi-stage, and trait-dependent sampling design and analysis to improve cost-efficiency by limiting the use of expensive molecular technologies to more informative individuals.
A second area of scientific collaboration has focused on the discovery of genetic determinants for complications of type 1 diabetes, building on infrastructure designed to follow participants of a pivotal randomized trial of alternate modes of insulin delivery. Led by geneticist Dr. AD Paterson, Hospital for Sick Children Research Institute, the group has conducted genome-wide association studies of a number of complex traits, including time to kidney and eye complications, blood sugar levels, cardiovascular risk factors, kidney function, and related traits. These studies, based on high-density sets of genetic susceptibility variants and longitudinal measurements of traits over extended follow-up, have motivated the development of new statistical methods by post-doctoral fellows and graduate students, and served as a testing ground for the evaluation and application of bias-reduced estimation, gene-based test statistics, and joint association models for longitudinal traits and time-to-event outcomes.
Shelley has also contributed to collaborative and applied research at a broad level through her work as an Associate Editor for the American Journal of Epidemiology (1991-98), The Canadian Journal of Statistics (2007-13), and Statistics in Medicine (2010 to present), as well as by service on grant review panels for NHRDP Doctoral Training Awards, CIHR Population Health Operating Grants, National Cancer Institute (Canada) Molecular, Environmental and Lifestyle Cancer, CIHR Genetics Operating Grants, and as grant reviewer for NSERC and other national and international agencies. She has served on the Board of Directors of the International Genetic Epidemiology Society, member and co-Chair of the Joint Priorities and Planning Committee in Genetic Epidemiology for the CIHR Institutes of Genetics and Population and Public Health, and is currently a member of the Advisory Committee to the Genetic Analysis Workshop funded by the US NIH.
An SSC member since her graduate student days, Shelley has served as Regional Representative on the Board of Directors (1989-91), Secretary of the Biostatistics Section (1992-95), Program Chair of the SSC Annual Meeting at Acadia University (2011), and as member on various SSC Committees.
Shelley lives in downtown Toronto with her husband, Wayne, who was a fellow undergraduate in residence at the University of Waterloo Conrad Grebel College. On weekends they attend to a 130-year-old farmhouse in rural Wellington County, which involves planting trees, weeding gardens, sometimes harvesting vegetables and, occasionally, reading in the shade.
The citation for the award reads:
"To Shelley Bull, for her outstanding contributions to research in medicine, public health, genetics and epidemiology; for the development of statistical methodology for these areas; and for her leadership, supervision, and mentorship in the Canadian statistical genetics and genetic epidemiology communities."