Applied Poisson Regression, Applied Survival Analysis and Advanced Topics in Logistic Regression

Monday, June 22, 2020 - Wednesday, June 24, 2020 8:30 AM - 12:30 PM

Download the syllabus for this course

Download the flyer for this course


Course Description

The aim of this workshop is to provide theoretical and practical training for biostatisticians, epidemiologists and professionals of related disciplines in statistical modeling with emphasis on logistic regression, Poisson Regression and Cox proportional hazards regression. Students will become familiar with the use of Stata to analyze data for these models.


Course Objectives

Upon successful completion of this course, students are expected to:

 

  1. Understand and Interpret the coefficients in logistic regression models
  2. Deal with confounding and effect modification in logistic regression models
  3. Understand strategies for building models in logististic regression
  4. Learn to assess the scale of continuous covariates in logistic regression
  5. Learn to recognize numerical problems when fitting logistic regression models
  6. Understand ways of assessing the performance of logistic regression models
  7. Understand and use logistic regression diagnostic statistics
  8. Understand the concept of person time data
  9. Review the Poisson Probability Distribution
  10. Learn how Poisson Regression may be used to model a dependent variable that consists of counts following a Poisson distribution
  11. Understand the unique character and challenges of time-to-event data
  12. Learn how to construct and interpret Kaplan Meier survival curves
  13. Explore the use of the Cox proportional hazards survival models
  14. Understand how to use Stata to obtain results for objectives (3), (5) and (6) above

 


Prerequisites

One year of biostatistics training including some logistic regression.


Course Textbooks - (reference only - not required)

Hosmer D, Lemeshow S, Sturdivant, RX (2013). Applied Logistic Regression, 3rd Ed. A Wiley-Interscience Publication, John Wiley & Sons Inc., New York, NY.

 

Selvin, S (1994). Practical Biostatistical Methods Duxbury Press

 

Hosmer D, Lemeshow S, May S (2008). Applied Survival Analysis: Regression Modeling of Time To Event Data, Second Edition. A Wiley-Interscience Publication, John Wiley & Sons Inc., New York, NY.


Instructor


Stanley Lemeshow, MSPH, PhD

Stan Lemeshow is Founding Dean of OSU's College of Public Health, serving in that capacity for 10 years (between 2003 and 2013).  He has been with the University since 1999 as a biostatistics professor in the School of Public Health and the Department of Statistics, director of the biostatistics core of the Comprehensive Cancer Center and Director of the University’s Center for Biostatistics. His biostatistics research includes statistical modeling of medical data, sampling, health disparities and cancer prevention.  He has published extensively in the applied and methodological literature and has co-authored three textbooks in the John Wiley & Sons Wiley statistics series, a leading publisher for the scientific, technical and medical communities worldwide. The textbooks he authored are: Applied Logistic Regression, (now in its 3rd edition), Applied Survival Analysis (now in its 2nd edition) and Sampling of Populations; Methods and Applications (now in its 4th edition).  In 2003, Dr. Lemeshow was awarded the Wiley Lifetime Award.  Professor Lemeshow maintains an ongoing relationship with Aarhus University, Denmark as an Honorary Professor in Biostatistics and is a member of the faculty of the Erasmus Summer Program, Rotterdam, Holland.  He has taught more than 100 short courses on biostatistical methods in this country and abroad, including eight European countries, Australia, China and India.



Course Fee

Late registration discount before May 1, 2020: $700.00
After May 1, 2020: $700.00


Register

The registration period has closed for this event.


Location

Zoom Webinar

The Zoom link for this live webinar course will be made available to course registrants prior to the start of class.


Share This