Categorical Data Analysis in Epidemiology
Biostatistics/Epidemiology 536
Course Outline, Fall Quarter
2007
Printable (pdf) version of this document
Lectures: T, Th 1:30-3:20 HST T733
Discussion: T
12:30-1:20 HST T639
Video
recording: Lectures will be taped using the tablet PC and made
available on the course web page. However,
this is subject to technology limitations and time so we cannot guarantee
availability of a recording for every class.
Handouts: Copies
of the handouts will be available from the class website (see below).
Instructor: Bill
Barlow
Research Professor of Biostatistics
williamb@crab.org or wbarlow@u.washington.edu
Telephone: 206-839-1761 (Cancer Research and
Biostatistics) Ü voice mail
Office
hours: T 11:00 am - 12:00 pm; Th 12:00 pm - 1:00 pm ; or other times
by appointment
(either at UW; Met Park East (M, W, F); or FHCRC (W))
Guest Instructor: Mary
Lou Thompson, Research
Professor of Biostatistics
Teaching
assistants Name E-mail Office
hours*
Charlotte Gard gardc@u.washington.edu
Colleen Sitlani cmg22@u.washington.edu
Britton Trabert brittont@u.washington.edu
Required text: Hosmer D and Lemeshow S. Applied
Logistic Regression, 2nd Edition.
Recommended text: Breslow N and Day
N. Statistical
Methods in Cancer Research, Volume1: The
Analysis of Case-Control Studies.
Required software: STATA 10. Can be purchased for $ 48 (small version, Getting Started manual) and up. The Intercooled version with permanent license ($155) is recommended. Details available from http://www.stata.com/order/new/edu/gradplan.html Order directly from Stata.
Computing: Access is
provided to Stata on computers in departmental
computing labs as well as the Health Sciences library computing
laboratory. Most students will use their
own personal computers.
Website: The
website will be used to distribute data, lecture notes, and to make important
broadcast announcements. The website
also allows you to send email to me, the TA's, or even the entire class. Our class specific website is http://courses.washington.edu/b536
. You will need your UW netid to get access to the website. Most handouts are in Adobe pdf format so can be printed directly by any computer with
Acrobat Reader.
E-mail: A classlist containing e-mail
addresses of all registered class members is being constructed for e-mail contact.
You are encouraged to direct questions to the instructor and/or teaching
assistant. When of general interest, these will be edited, rendered anonymous
as to the sender and forwarded together with the response to the classlist.
Grading: 25% Assignments
5%
Class participation
20% Midterm (In
class on October 30)
25% Project (Two
parts: first part due Nov. 8; second part due Nov. 29)
25% Final (In
class on the last day of lectures, December 6)
These percentage
are used to find your rank in the class and the ranks are used to assign the
final grade.
Lectures: The lectures are prepared in advance, with hardcopies of
the lecture notes distributed in class and posted to the website. Questions
from registered students are encouraged.
The questions often clarify points on which several students may share
the same uncertainty. If you believe your question is not of general interest,
feel free to ask your question before or after class. Auditors may not submit
assignments or exams for grading.
Discussion section: The discussion section will be used to discuss Stata examples, homework, and outstanding questions.
Assignments will generally be distributed one week in advance and be due in class the following Thursday. Computer output should be edited to eliminate all irrelevant material and should clearly indicate the answer to the question posed. Late assignments are not acceptable. Homework keys prepared by the teaching assistants will be posted to the class website. Assignments are not to prepare you for exams, but to prepare you for realtime application of the methods to data.
Difficulty: Most
will find this course demanding. The homeworks are time-consuming and the text needs to be read
several times for it to make sense. We
expect you to talk to your classmates about the materials and homeworks to gain further insight. If you need help, please use both the TA and
myself to get assistance. I welcome
feedback during the course so feel free to let me know what you think by
e-mail, before or after class, or by anonymous note.
It is assumed that when entering BIOST/EPI 536, you have completed a course in linear regression and been exposed to logistic regression and some categorical data analysis. You should understand the basic statistical concepts of sampling variation, parameter estimation, confidence limits, and statistical hypothesis testing. You should know about simple statistical techniques for analyzing data from a binomial distribution including odds ratio estimation in 2 x 2 tables and in series of 2 x 2 tables. You should be familiar with the Mantel-Haenszel test and testing trend in a 2 x K table. By the end of this course, you should be able to do the following using unconditional or conditional logistic regression, or using generalized estimating equations (GEE):
1.
Perform regression
analyses with multiple predictors.
2.
Perform tests that
indicate which covariates should be included in the model.
3.
Determine if there is
a linear trend for ordinal level (or better)
covariates
4.
Use graphical and
other methods for assessing adequacy of the fitted model.
5.
Interpret each
coefficient in the model.
6.
Describe the methods
and results to a non-statistical reader.
Books at the Health Sciences Reserve Desk
(3 day):
Hosmer D and Lemeshow S. Applied Logistic Regression, 2nd
Edition.
Breslow N and Day N.
Statistical Methods in Cancer
Research, Volume1: The Analysis of Case-Control Studies.
Clayton
D and Hills M, Statistical Models in
Epidemiology.
Kleinbaum DG and Klein M. Logistic Regression: A Self-Learning
Special Dates
October 2 Discussion section (12:30-1:20) will meet
in the HS Library Computer Labs A and B
October 11, 16, 18, 23 Lectures by Dr. Mary Lou Thompson
October 30 Midterm exam (in class)
November 8 Part 1 of the project due
November 29 Part 2 of the project due
December 6 Final exam on the last day of lectures (in class)