Time Schedule:
Anneclaire Jenice De Roos
EPI 515
Seattle Campus
Introduces advanced epidemiologic methods, including casual modeling, inverse probability weighting, propensity scores, sensitivity analysis, imputation for missing data, approaches to multiple comparisons, Bayesian adjustment of risk estimates, recursive portioning, and modeling for prediction. Prerequisite: EPI 512; EPI 513; EPI 514; EPI 536/BIOST 536. Offered: jointly with BIOST 519; Sp.
Class description
This quarter (Autumn 2008) we will cover topics including propensity scores, missing data imputation, sensitivity analysis, Bayesian shrinkage for estimation, approaches to multiple comparisons, and data mining methods.
Student learning goals
Critically read and interpret epidemiologic studies that apply advanced methods for analysis and interpretation
Understand the purpose of various advanced epidemiologic methods, and identify appropriate situations for their application
Develop a unified framework that recognizes the how advanced methods relate to more traditional epidemiologic methods
Demonstrate the ability to self-direct learning about a specific analytical problem and critically evaluate alternative statistical methods to apply to the situation
Effectively converse with epidemiologist and biostatistician colleagues about application of advanced statistical methods, in a manner necessary for successful collaboration
General method of instruction
The class will be structured with short lectures complemented by in-depth class discussions.
Recommended preparation
EPI 512-514, EPI 536
Class assignments and grading
