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.