Biostatistics Modeling and Methods - PubH 7402

Spring 2009, 4 credits

INSTRUCTOR: Melanie M. Wall, Division of Biostatistics, A426 Mayo Building, 625-2138 melanie@biostat.umn.edu
Office hours: 2:00-3:00, Mondays, Mayo A426

TEACHING ASSISTANT: Rajarshi Guha Niyogi guhan003@umn.edu
Office hours: held in C381 computer lab: Tuesday 4-5 and Wed 1:30-2:30

TIME AND PLACE: 12:20-2:15, T & Th, Location: Mayo 3-100.

COURSE DESCRIPTION: This course is the second part of a two-course sequence intended for PhD students in the School of Public Health who need a rigorous approach to probability and statistics and statistical inference with applications to research in public health. This course is about modeling data and using the estimates of those models along with estimates of uncertainty of those models to answer scientific research questions. Statistical computing will be performed using either SAS or R.

COURSE WEEKLY OUTLINE:

1. General linear model (4 weeks)
   • simple linear - OLS
   • multiple regression including dummy variables
   • diagnostics, model selection, heteroskedasticity, measurement error
2. Generalized linear model (4 weeks)
   • Binomial and logistic regression
   • Multinomial response models - proportional odds models, generalized logistic regression (i.e. discrete choice models), diagnostics
   • Poisson regression, overdispersion, diagnostics
3. Midterm exam - Thursday March 12
4. Spring BREAK - March 16-20
5. Methods for correlated data (4 weeks)
   • continuous and discrete outcomes, mixed effect modeling and GEE
   • clustered data (e.g. patients within clinics, clinics within health plans)
   • longitudinal data repeated measures, lagged dependent variables, time varying covariates, autocorrelation
6. Methods for censored data (3 weeks)
   • Survival data models
   • Kaplan Meier, life tabl es
   • Cox proportional hazards
7. Final exam - Tuesday May 12, 1:30-3:30

COURSE OBJECTIVES: Upon completion of this course, the student should understand the fundamentals of generalized linear models for continous, ordinal and categorical data. The students should also understand how to model data coming from correlated sampling structures and how to deal with censored data. This means comprehending the concepts of model construction, applying these concepts to actual datasets, and implementing the appropriate model in standard statistical software.

PREREQUISITES: PubH 7401 or permission of the instructor.

METHOD OF INSTRUCTION AND WORK EXPECTATIONS: Classroom experience will be a combination of in-class lecture and in-class hands-on computer lab experience (using SAS and R) in the SPH Computer Lab Mayo C381 or in Mayo 3-100 with laptops. Students may discuss homeworks with other class-mates but are expected to write-up homeworks independently.

RECOMMENDED TEXTS:

• Recommended but not required (available at bookstore)
  • Statistical Analysis and Data Display: An Intermediate Course with Examples in S-PLUS, R, and SAS by Heiberger, R.M. and Holland, B.
• Optional texts (these are on reserve in the Biostat reading room and the Biomed library)
  • An Introduction to Generalized Linear Models by Dobson, A.J.
  • Biostatistical Methods: The Assessment of relative risks, by Lachin, J.M.
  • Biostatistics: A Methodology for the Health Sciences, 2nd Ed. by van Belle, G., Heagerty, P.J., Fisher, L.D., and Lumley, T.S.
  • Regression Methods in Biostatistics: Linear Logistic, Survival, and Repeated Measures Models, by Vittinghoff, E., Glidden, D.V., Shiboski, S.C., and McCulloch, C.E.

COMPUTING: The course will present all methods using both SAS and R. Both softwares are comprehensive statistical tools capable of performing most all of the traditional and recently advanced existing statistical methodology. They differ greatly in their interface and programming logic, also they differ economically as SAS is a commercial for profit company and R is a user developed software and is available for free. SAS is still considered the standard used in government and throughout the healthy care research and delivery industry. For example, the FDA still requires all pharmaceutical companies to perform their analysis using the SAS software as it has been vetted and certified. Nevertheless the fact that R is free and is ever expanding with user created add-ons available has made it also very popular. Here's a website that entices SAS and SPSS user to start using R - http://rforsasandspssusers.com . Also, here are fantastic online learning tools for SAS http://www.ats.ucla.edu/stat/sas/ and R http://www.ats.ucla.edu/stat/r/ . Another great reference for using R is called EpiTools and is found here

Students are welcome to choose either of the two softwares to perform analyses for homework in this course and can change at any time.

Accessing SAS:
The annual license for PC-SAS through the University of Minnesota is $120: http://www1.umn.edu/adcs/site/list.html if you want to buy it yourself to put on your own computer. Or, in addition to Mayo C-381 (which will only be open for TA office hours), you can find SAS on computers at the following public computing labs: Deihl Hall Biomedical Library, Coffman Union B060, School of Public Health Student Lounge, HHH Room 50 - West Bank

Accessing R:
Since R is free and available from the web, you can download it to your computer or a computer you have access to from http://www.cran.r-project.org/

ASSESSMENT and GRADES:

• Homework 50% (Homework will be due approximately every week on Thursday)
  Homework is due in class (i.e. 12:20 p.m.). Homework turned in after the time it is due will have 50% deducted. You may only turn in late homework up until the next homework is due, after that there will be no credit given. Students may discuss homeworks with other class-mates but are expected to write-up homeworks independently.
• One midterm exam 20% (In class March 12th)
• One final exam 30% (In class May 12th)
• GRADES will be assigned by combining Homework, Midterm and Final exam according to the percentages mentioned (i.e. Homework 50%, Midterm 20%, Final Exam 30%).
  The grading scale based on the Total percentage is the following:
  • 93-100 A , 90-92 A-, 85-89 B+, 78-84 B, 75-77 B-, 70-74 C+, 65-69 C, 60-64 C-
  • For those taking the course S/N (Pass/Fail), an S will be earned if a grade equivalent to a C- or above is achieved.
  • For all students, if a grade of at least C- is not achieved, the grade will be F (or N).
  • The grading option (i.e. A/F or S/N) can ONLY be changed during the first two weeks of the semester. It CANNOT be changed after that.
  • An incomplete grade is permitted only in cases of extraordinary circumstances and following consultation with the instructor. In such cases an "I" grade will require a specific written agreement between the instructor and student specifying the time and manner in which the student will complete the course requirements. Extension for completion of the work will not exceed on year.
  • Withdrawal Policy
    • School of Public Health students may withdraw from a course through the second week of the semester without permission. No "W" will appear on the transcript.
    • After the second week students are required to do the following:
      1. The student must contact and notify their advisor and course instructor informing them of the decision to withdraw form the course.
      2. The student must send an e-mail to the SPH Student Services Center (SSC). The email must provide the student name, ID#, course number, section number, semester and year with instructions to withdraw the student form the course, and acknowledgment that the instructor and advisor have been contacted.
      3. The advisor and instructor must email the SSC acknowledging the student is canceling the course. All parties must be notified of the student's intent.
      4. The SSC will complete the process by withdrawing the student from the course after receiving all emails (student, advisor, and instructor). A "W" will be placed and remain on the student transcript for the course.
    • After discussion with their advisor and notification to the instructor, students may withdraw up until the eight week of the semester. There is no appeal process.

DISABILITY ACCOMMODATION It is University policy to provide, on a flexible and individualized basis, reasonable accommodations to students who have documented disability conditions (e.g., physical, learning, psychiatric, vision, hearing, or systemic) that may affect their ability to participate in course activities or to meet course requirements. Students with disabilities are encouraged to contact Disability Services for a confidential discussion of their individual needs for accommodations. Disability Services is located in Suite 180 McNamara Alumni Center, 200 Oak Street. Staff can be reached by calling 612/626-1333 voice or TTY. The website is http://ds.umn.edu

STUDENT CONDUCT, SCHOLASTIC DISHONESTY, AND SEXUAL HARASSMENT POLICY Students are responsible for knowing the University of Minnesota, Board of Regents' policy on Student Conduct and Sexual Harassment found at www.umn.edu/regents/polindex.html. Students are responsible for maintaining scholastic honesty in their work at all times. Students engaged in scholastic dishonesty will be penalized, and offenses will be reported to the Office of Student Academic Integrity (OSAI, www.osai.umn.edu). The University's Student Conduct Code defines scholastic dishonesty as "plagiarizing; cheating on assignments or examinations; engaging in unauthorized collaboration on academic work; taking, acquiring, or using test materials without faculty permission; submitting false or incomplete records of academic achievement; acting alone or in cooperation with another to falsify records or to obtain dishonestly grades, honors, awards, or professional endorsement; or altering, forging, or misusing a University academic record; or fabricating or falsifying of data, research procedures, or data analysis." Plagiarism is an important element of this policy. It is defined as the presentation of another's writing or ideas as your own. Serious, intentional plagiarism will result in a grade of "F" or "N" for the entire course. For more information on this policy and for a helpful discussion of preventing plagiarism, please consult University policies and procedures regarding academic integrity: http://writing.umn.edu/tww/plagiarism/ Students are urged to be careful that they properly attribute and cite others' work in their own writing. For guidelines for correctly citing sources, go to http://tutorial.lib.umn.edu/ and click on "Citing Sources". In addition, original work is expected in this course. It is unacceptable to hand in assignments for this course for which you receive credit in another course unless by prior agreement with the instructor. Building on a line of work begun in another course or leading to a thesis, dissertation, or final project is acceptable. If you have any questions, consult the instructor.

Division of Biostatistics
A460 Mayo Building MMC 303
420 Delaware Street S.E.
Minneapolis, MN 55455
(612)625-2138
melanie@biostat.umn.edu