Biometry 755 
Regression Methods for  Clinical Research


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MUSC Department of Biostatistics, Bioinformatics and Epidemiology Home Page


 

 

 

Welcome to the 2009 Winter/Spring homepage for Biometry 755.

Instructor:  Elizabeth G. Hill, Ph.D.
Office:  Hollings Cancer Center, 118D
Email:  hille@musc.edu
Phone:  876-1115
Fax:  792-4233
Office Hours:  By appointment

TA:  Megan Schuler
Office:  135 Cannon Place, 305W-A
Email:  schuler@musc.edu
Office Hours:  By appointment

Lecture:  T/Th 12:00-2:00pm, Cannon Place Room 301

Course website:  http://www.musc.edu/~hille/BMTRY755

Prerequisites: Biometry 700, Biometry 736

Textbook:  Common Statistical Methods for Clinical Research with SAS Examples, 2nd Edition.  Glenn A. Walker, 2002.

Objectives: Upon completion of this course, the student will be able to:

  • Choose appropriate methods, models, and hypothesis tests for data analyses related to linear regression, logistic regression and survival analysis.
  • Check the underlying assumptions for each method.
  • Conduct diagnostics and goodness-of-fit for each method.
  • Select an appropriate model.
  • Interpret results.
  • Run SAS programs related to these types of analyses and interpret the output.
  • Appreciate and gain an understanding of the presentation of statistical findings in literature relevant to their area of expertise.

Evaluation

  • Class Participation – 10%
  • Homework – 25%
  • Take home exam – 20%
  • Journal club (group) – 30%
  • Journal club (individual) – 15%

Important Dates

  • Tuesday, January 20th – Last day to drop or add classes
  • Thursday, February 19th – Take home exam distributed at end of class
  • Friday, February 27th – Exam due by 5pm
  • March 9th – 13th - MUSC Spring Break
  • Tuesday, April 28th – Last day of class

 

Class Participation
All students are expected to attend class regularly.  As a courtesy to both your instructor and your fellow classmates, please make every effort to be prompt, as class will begin at the scheduled time of 12:00pm.  The course covers a tremendous amount of material and all classes will be used for lecture or lab activities.  Ten percent of the course grade is based on your regular participation and all students are awarded ten points at the start of the semester.  Some absences, however, are unavoidable.  In an effort to accommodate our busy professional and personal lives, all students can miss three classes without penalty.  Note that an absence is defined as missing more than 30 minutes of any scheduled lecture.  One point is deducted from the class participation grade for each subsequent absence.  Special circumstances (e.g. birth or death in family, family illness) will be dealt with on an individual basis.

Homework
There will be roughly 10 homework problems assigned throughout the semester.  Occasionally you will be given time in class to work on the assigned problems.  Homework should be submitted electronically to Megan Schuler.  If you wish to write your homework and then send a scanned electronic copy, please be sure your handwriting is legible.  Work that is illegible will not be graded.  The final homework grade will be based on the student’s best 7 out of 10 assignments.  Because of this policy, late homework is NOT accepted – no exceptions.

 

Computing
The course uses SAS version 9.1.  You should bring your laptop to class with the software loaded on your machine as we will be working examples together throughout the lecture.  Additionally, there will often be time to begin homework in class.

 

Data
I am always looking for interesting data and would be happy to use data you provide (not the data for your project) to demonstrate concepts in class.

Course Topics

  1. Simple Linear Regression
    1. Assumptions
    2. Least Squares
    3. Goodness of fit
    4. Inference about slope, intercept, straight line
    5. Prediction
    6. Correlation
    7. ANOVA table
  2. Multiple Linear Regression
    1. Assumptions
    2. Goodness of fit
    3. ANOVA table
    4. Hypothesis testing
    5. Multiple, partial, and multiple partial correlation
    6. Confounding
    7. Interaction
    8. Diagnositcs
    9. Dummy (indicator) variables
  3. Logistic Regression
    1. Binary data
    2. The logit function
    3. Maximum likelihood
    4. Multiple logistic regression
    5. Hypothesis testing
    6. Interpretation of regression coefficients for continuous covariates
    7. Interpretation of regression coefficients for nominal/ordinal covariates
    8. Odds ratios
    9. Goodness of fit
  4. Survival Analysis
    1. Time-to-event data
    2. Censoring
    3. Survival function
    4. Hazard function
    5. Estimation of survival and hazard
    6. Life-table
    7. Kaplan-Meier
    8. Cumulative hazard
    9. Log-rank test
    10. Stratified tests
    11. Proportional hazards models
    12. Hypothesis testing
    13. Time-varying covariates
    14. Hazard ratios
  5. Other topics (as time permits)
    1. Analysis of variance
    2. Generalized Estimating Equations
    3. Linear mixed models
    4. Analysis of complex sample survey data

 

Bibliography

  1. How to Report Statistics in Medicine:  Annotated Guidelines for Authors, Editors, and Reviewers, 2nd Edition.  T.A. Lang and M. Secic, 2006, American College of Physicians, Philadelphia, PA.
  2. Multivariable Analysis:  A Practical Guide for Clinicians, 2nd Edition.  M.H. Katz, 2006, Cambridge University Press, New York.
  3. Applied Logistic Regression, Second Edition.  D.W. Hosmer and S. Lemeshow, 2000, John Wiley & Sons, Inc., New York.
  4. Applied Survival Analysis.  D.W. Hosmer and S. Lemeshow, 1999, John Wiley & Sons, Inc., New York.
  5. Statistical Methods in Medical Research, Fourth Edition.  P. Armitage, G. Berry and J.N.S. Matthews, 2002, Blackwell.
  6. SAS for Linear Models, Fourth Edition.  Littell, Stroup and Freund, 2002, SAS Institute, Cary, NC.
  7. Logistic Regression Using the SAS System.  P.D. Allison, 1991, SAS Institute, Cary, NC.
  8. Survival Analysis Using the SAS System.  P.D. Allison, 1995, SAS Institute, Cary, NC.
  9. The Little SAS Book:  A Primer, Third Edition.  L.D. Delwiche and S.J. Slaughter, 2003, SAS Institute, Cary, NC.

 

 

 

 

 

 

 

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