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The study of population health often involves the use of observational data from existing data sets, complex survey designs and longitudinal follow-up. Ordinary regression analysis, familiar to most researchers and practitioners is inadequate for analyzing such data and answering important questions about the relationship of risk factors to health. Nonstatisticians such as epidemiologists and health services researchers require a working knowledge of the sophisticated modeling techniques used by professional statisticians. Quantitative Methods in Population Health provides an accessible guide for students in an applied statistics sequence as well as for practicing researchers and professionals.

Mari Palta's timely text assumes some background in mathematics and in applied statistics and regression analysis, but little knowledge of statistical theory. The Statistical Analysis System(R) (SAS) is an ubiquitous tool in the field, and some basic knowledge of its structure is assumed. Each topic starts with an explanation of the theoretical background that is necessary for understanding the technique as well as for establishing a basis to adopt more advanced methods or software in the future. The author endeavors to keep the material immediately applicable by providing detailed instructions for how to run and interpret procedures in SAS. Topics covered include: Regression analysis with weights Unequal variance Correlated and longitudinal outcomes Mixed effects Generalized linear models Generalized estimating equations

SAS commands for applying the methods-including PROC REG, PROC MIXED, and PROC GENMOD-areprovided, and each section includes real-life examples. Quantitative Methods in Population Health proves a seamless meshing of the theoretical and practical in this vital field.