Applied Logistic Regression
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This Applied Logistic Regression course provides theoretical and practical training for epidemiologists,
biostatisticians and professionals of related disciplines in statistical modeling with
particular emphasis on logistic regression.
The increasingly popular logistic regression model has become the standard method for regression analysis of binary response data in the health sciences.
By the end of this course, students should
- Master methods of statistical modeling when the response variable is binary.
- Be confident users of the Stata package for computing binary logistic regression
This is a hands-on, applied course where students will become proficient at using
computer software to analyze data drawn primarily from the fields of medicine,
epidemiology and public health.
There will be many practical examples and homework exercises in this class to help you learn. If you fully apply yourself in this course and complete all of the homework, you will have the opportunity to master various methods of statistical modeling and you will become a more confident user of the Stata* package for computing linear, polynomial and multiple regression.
*Access to Stata will be provided at no cost for the duration of this course.Note: Enrollment for this course will close on Wednesday, May 13, 2015.
This course was developed in partnership with the Centre Virchow-Villermé for Public Health Paris-Berlin
, a bi-national centre of the Université Sorbonne Paris Cité
and Charité – Universitätsmedizin Berlin
. Special support was contributed from the Université Paris Descartes
that also belongs to the community of Université Sorbonne Paris Cité.
- Logistic Regression Analysis
- Fitting the Logistic Model
- The Likelihood Ratio Test
- Finding a Confidence Interval for β and π
- The Multiple Logistic Regression Model
- Fitting the Multiple Logistic Regression Model
- Confidence Intervals for β, the Logit, and π
- Interpretation of Coefficients
- Dichotomous Independent Variables
- Polychotomous Independent Variables
- Continuous Independent Variables
- Statistical Adjustment
- Interaction and Confounding
- Estimating Odd Ratios in the Presence of Interaction
- The 2X2 Table - Stratified Analysis vs Logistic Regression
- Assessing the Fit of the Logistic Regression Model
- Summary Measures of Goodness-of-Fit
- Area Under the ROC Curve
- R-squared-type Measures
- Numerical Problems
- Estimating the Mortality of ICU Patients