Dr. Graubard received a Ph.D. in mathematics from the University of Maryland in 1991. He began his career as a mathematical statistician at the National Center for Health Statistics in 1977, and held research positions at the Alcohol Drug Abuse and Mental Health Administration and the National Institute of Child Health and Human Development. Dr. Graubard joined the National Cancer Institute in 1990. He received the American Statistical Association (ASA) and Biometric Society Snedecor Award for Applied Statistical Research in 1990, and he is a Fellow of the American Statistical Association and of the Statistics Section of the American Association for the Advancement of Science. In 2020, he was recognized with the ASA Mentoring Award. In 2023, he retired from government service, and continues to support DCEG as a special volunteer.
Dr. Graubard’s research interests were primarily in the development and application of statistical methods to efficiently design and analyze studies that can make valid inferences to well-defined target populations. These types of studies often collect data using complex sample designs that randomly sample individuals using stratified and multistage cluster sampling. National cross-sectional health surveys and population-based study designs such as case-control studies that sample population controls from the population at risk of becoming diseased and case-cohort or nested cohort studies that subsample from a cohort can use various complex sample designs. In addition, national health surveys link the participants to vital statistics or electronic medical records to form population representative cohorts to study disease etiology. When analyzing data from these types of studies attention needs to be given to the complex sample design because the sample weights that reflect the differential rates of selection of study participants and the stratification and clustering of the sampling that are used for efficient data collection can affect the estimation of prevalence, incidence, risk factor associations, absolute risk of diseases, and the variances in analyses.
Another area of Dr. Graubard’s research was the use of external surveys or other types of studies to predict variables not measured in large cohorts or cross-sectional surveys. Dr. Graubard has considered the implications of utilizing the National Health and Nutrition Examination Survey (NHANES) as the external data source for fitting the prediction model.
In summary, a large part of Dr. Graubard’s research involved the integration of survey methods to develop statistical methods for efficiently using complex sample designs in cancer surveillance and epidemiology.
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