Syllabus of Population Health
Sciences 800
Quantitative Methods in
Population Health I
Spring
2003
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Instructor: |
Guan-Hua
Huang, Ph.D. |
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Office:
703 WARF |
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Phone:
608-265-6176 |
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Teaching
assistant: |
Amanda
Riemer |
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Office:
752 WARF |
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Phone:
608-262-6028 |
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Email:
amriemer@wisc.edu
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Class
meetings: |
Lecture:
Tuesday and Thursday 9:30-10:20 am at 758 WARF |
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Lab:
Thursday 12:30-2:00 pm at 758 WARF |
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Office
hours: |
Instructor:
Thursday 4:15-5:15 pm |
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TA:
Wednesday 1:00-3:00 pm |
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Course
website: |
The goals of this course are to introduce
regression
analysis for continuous and discrete data, and data analyses that integrate the
methods learned in Stat 541 and PHS 650 sec. 2. Topics include measures of
association, simple and multiple linear regressions, inference for regression
coefficients, confounding and interaction, regression diagnostics, logistic
regression, and conditional logistic regression.
The
course consists of lectures and laboratory sessions. The lectures are given on
Tuesday and Thursday mornings. The
lectures will primarily review and reinforce major issues. There is a laboratory
session on Thursday afternoon. The laboratory exercise will be distributed prior
to each class, and students are expected to read each lab exercise at home. Each
student will be assigned to a lab group and discuss the exercise with group
members in the lab. At the end of the lab, there will be a seminar-type
discussion. Each group is required to hand in a write-up of laboratory
problems.
The
course uses the SAS software for statistical computing. Students are expected to
be familiar with the usage of the software.
Handouts
corresponding to each lecture will be available on the course website before
each class. The required textbooks for this course are
Kleinbaum DG, Kupper LL,
Muller KE and Nizam A: "Applied Regression Analysis and Other Multivariable
Methods" 3rd Edition, Duxbury Press, 1998.
Mari Palta: “Quantitative
Methods in Population Health”- available from Bob’s Copy
Shop.
The
course grade will be based on homeworks (25%), write-ups of lab problems (20%),
one midterm exam (25%), and one final exam (30%). The midterm exam will be held
on March 13 (12:30-2:00 pm), and the final exam will be during finals week. Both
exams are open book.
WEEK-BY-WEEK
OUTLINE
1.
Readings refer to
Kleinbaum, Kupper, Muller and Nizam: “Applied Regression Analysis and Other
Multivariable Methods” 3rd Edition, Duxbury Press, 1998 (KKMN), and Palta:
“Quantitative Methods in
Population Health” (Palta).
2.
Homework data sets are
handed out on Thursdays. Guidelines for assignment preparation should be
followed.
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Week
1 |
Lecture |
Review |
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Jan
21, 23 |
Lab |
Lab
group assignment |
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Reading |
KKMN
Chapter 3; Palta Modules 0 and 1 |
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Assignment |
NA |
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Week
2 |
Lecture |
Basics
of linear regression |
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Jan
28, 30 |
Lab |
Basic
statistics |
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Reading |
KKMN
Chapter 5 except 5-10; Palta Module 2 |
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Assignment |
NA |
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Week
3 |
Lecture |
Correlation
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Feb
4, 6 |
Lab |
Simple
linear regression |
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Reading |
KKMN
Chapter 6 except 6-3 and 6- 7; Palta Module 3 |
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Assignment |
Homework
1 due on Feb 6 |
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Week
4 Feb
11, 12, 13 |
Lecture |
The
ANOV A table (2/11), multiple regression (2/12), partial F-test
(2/13) |
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Lab |
Correlation
and linear regression |
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Reading |
KKMN
Chapters 7, 8 and 9; Palta Modules 4, 5 and 6 |
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Assignment |
NA |
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Week
5 |
Lecture |
No
class |
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Feb
20 |
Lab |
Multiple
linear regression and direct standardization
(2/20) |
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Reading |
NA |
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Assignment |
Homework
2 due on Feb 20 |
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Week
6 Feb
25, 26, 27 |
Lecture |
Polynomial
regression and indicator variables (2/25), interaction and confounding
(2/26, 2/27) |
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Lab |
Partial
F-test, polynomial terms and dummy variables |
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Reading |
KKMN
13-1 through 13-6 and 14-1 through 14-3, Chapter 11, 14-4 through 14-9;
Palta Module 7, 8 and 9 |
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Assignment |
NA |
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Week
7 |
Lecture |
Regression
diagnosis |
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Mar
4, 6 |
Lab |
Interaction
and confounding |
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Reading |
KKMN
Chapter 12 |
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Assignment |
NA |
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Week
8 |
Lecture |
Midterm
review |
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Mar
11, 13 |
Lab |
Midterm
exam |
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Reading |
NA |
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Assignment |
Homework
3 due on Mar 11 |
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Week
9 |
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Spring
break |
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Mar
18, 20 |
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Week
10 |
Lecture |
Review
of exam, properties of relative risk and odds
ratio |
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Mar
25, 27 |
Lab |
Variable
selection in epidemiologic analysis |
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Reading |
Palta
Module 12 |
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Assignment |
NA |
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Week
11 Apr
1, 3 |
Lecture |
Significance
testing in 2x2 and 2xk table, confidence intervals for odds
ratio |
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Lab |
The
2X2 table |
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Reading |
Palta
Modules 11, 13 and 14; supplemental text |
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Assignment |
NA |
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Week
12 Apr
8, 10 |
Lecture |
Introduction
to logistic regression, maximum likelihood
estimation |
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Lab |
Analysis
of contingency tables |
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Reading |
KKMN
pages 656-660 and Chapter 22; Palta Modules 15 and
16 |
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Assignment |
NA |
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Week
13 Apr
15, 17 |
Lecture |
Control
of confounding with logistic regression, interaction effects in logistic
regression |
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Lab |
Logistic
regression |
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Reading |
KKMN
pages 660-671; Palta Modules 17 and 18 |
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Assignment |
NA |
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Week
14 |
Lecture |
Logistic
regression for contingency tables |
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Apr
22, 24 |
Lab |
Confounding
and interaction |
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Reading |
Palta
Module 19 |
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Assignment |
Homework
4 due on Apr 24 |
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Week
15 |
Lecture |
Goodness
of fit of logistic regression |
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Apr
29, May 1 |
Lab |
Likelihood
ratio test and goodness-of-fit |
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Reading |
Palta
Module 20 |
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Assignment |
NA |
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Week
16 May
6, 8 |
Lecture |
Logistic
regression of case-control data and conditional logistic
regression |
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Lab |
Review
of final exam |
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Reading |
KKMN
23-5-2; Palta Modules 21 and 22 |
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Assignment |
Homework
5 due on May 8 |
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Week
17 |
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In
class final exam during finals week |
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