Advanced Statistics

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stat 4620/5620: DATA ANALYSIS

The course begins with a thorough description of linear models and generalized linear models (GLM). Additive and generalized additive models (GAM) is then introduced, followed by their mixed model extensions. Spatial point process approaches will also be demonstrated. Ecological data sets will be used throughout to both demonstrate and validate the suggested analytical tools.


stat 5550: Analysis of longitudinal data

This course is concerned with statistical techniques for analysis of longitudinal data, data that are collected repeatedly over a time on a number of subjects. Topics include generalized estimating equations; fixed, random and mixed effects linear models; generalized linear models; diagnostics and model checking; as well as missing data issues.



Spatial analysis includes a variety of techniques, many still in their early development, using different analytic approaches and applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is the technique applied to structures at the human scale, most notably in the analysis of geographic data.



Statistical methods for assessing risk are discussed, including dose-response models, survival analysis, relative risk analysis, bioassay, estimating methods for zero risk trend analysis and association risks. Case studies are used to illustrate the methods.



This course advances and reinforces the topics learned in PHAR 2010.03. The first term focuses on research methods and biostatistics seen in various trial designs. Students learn to critically evaluate the medical literature and write a term paper reviewing the evidence behind a clinical decision. The second term will focus on applying the tenets of evidence-based clinical practice. Through a journal club setting, students will evaluate the strengths and weaknesses seen in the literature as they relate to a clinical situation. Students are expected to use these skills in their problem-based learning classes.