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Lecture 18: Multiple Logistic Regression

Lecture 18: Multiple Logistic RegressionMulugeta Gebregziabher, 701/755: Biostatistical methods II Spring 2007 Department of Biostatistics, Bioinformatics and EpidemiologyMedical University of South CarolinaLecture 18: Multiple Logistic Regression p. 1/48 Topics to be covered Review1. Purpose of empirical models: Association vs Prediction2. Design of observational studies: cross-sectional, prospective, case-control3. Randomization, Stratification and Matching Multiple Logistic regression1. The model2. Estimation and Interpretation of Parameters3. Confounding and Interaction4. Effects of omitted variables5. Model Fitting Strategies6. Goodness of Fit and Model Diagnostics Matching (group and individual) Conditional vs Unconditional analysis methods III: Advanced Regression MethodsLecture 18: Multiple Logistic Regression p. 2/48 Review: Purpose of empirical modelsEmpirical models: are models that are fitted to provide succinct descriptions of relationshipsobserved in data.

Lecture 18: Multiple Logistic Regression Mulugeta Gebregziabher, Ph.D. BMTRY 701/755: Biostatistical Methods II Spring 2007 Department of Biostatistics, Bioinformatics and Epidemiology Medical University of South Carolina Lecture 18: Multiple Logistic Regression – p. 1/48

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Lecture 18: Multiple Logistic Regression

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