Syllabus of Population Health
Sciences 800
Quantitative Methods in
Population Health I
Spring
2003
Instructor: 
GuanHua
Huang, Ph.D. 

Office:
703 WARF 

Phone:
6082656176 


Teaching
assistant: 
Amanda
Riemer 

Office:
752 WARF 

Phone:
6082626028 

Email:
amriemer@wisc.edu

Class
meetings: 
Lecture:
Tuesday and Thursday 9:3010:20 am at 758 WARF 

Lab:
Thursday 12:302:00 pm at 758 WARF 
Office
hours: 
Instructor:
Thursday 4:155:15 pm 

TA:
Wednesday 1:003:00 pm 
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 seminartype
discussion. Each group is required to hand in a writeup 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%), writeups of lab problems (20%),
one midterm exam (25%), and one final exam (30%). The midterm exam will be held
on March 13 (12:302:00 pm), and the final exam will be during finals week. Both
exams are open book.
WEEKBYWEEK
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.
Week
1 
Lecture 
Review 
Jan
21, 23 
Lab 
Lab
group assignment 

Reading 
KKMN
Chapter 3; Palta Modules 0 and 1 

Assignment 
NA 



Week
2 
Lecture 
Basics
of linear regression 
Jan
28, 30 
Lab 
Basic
statistics 

Reading 
KKMN
Chapter 5 except 510; Palta Module 2 

Assignment 
NA 



Week
3 
Lecture 
Correlation

Feb
4, 6 
Lab 
Simple
linear regression 

Reading 
KKMN
Chapter 6 except 63 and 6 7; Palta Module 3 

Assignment 
Homework
1 due on Feb 6 



Week
4 Feb
11, 12, 13 
Lecture 
The
ANOV A table (2/11), multiple regression (2/12), partial Ftest
(2/13) 

Lab 
Correlation
and linear regression 

Reading 
KKMN
Chapters 7, 8 and 9; Palta Modules 4, 5 and 6 

Assignment 
NA 



Week
5 
Lecture 
No
class 
Feb
20 
Lab 
Multiple
linear regression and direct standardization
(2/20) 

Reading 
NA 

Assignment 
Homework
2 due on Feb 20 



Week
6 Feb
25, 26, 27 
Lecture 
Polynomial
regression and indicator variables (2/25), interaction and confounding
(2/26, 2/27) 

Lab 
Partial
Ftest, polynomial terms and dummy variables 

Reading 
KKMN
131 through 136 and 141 through 143, Chapter 11, 144 through 149;
Palta Module 7, 8 and 9 

Assignment 
NA 



Week
7 
Lecture 
Regression
diagnosis 
Mar
4, 6 
Lab 
Interaction
and confounding 

Reading 
KKMN
Chapter 12 

Assignment 
NA 



Week
8 
Lecture 
Midterm
review 
Mar
11, 13 
Lab 
Midterm
exam 

Reading 
NA 

Assignment 
Homework
3 due on Mar 11 



Week
9 

Spring
break 
Mar
18, 20 





Week
10 
Lecture 
Review
of exam, properties of relative risk and odds
ratio 
Mar
25, 27 
Lab 
Variable
selection in epidemiologic analysis 

Reading 
Palta
Module 12 

Assignment 
NA 



Week
11 Apr
1, 3 
Lecture 
Significance
testing in 2x2 and 2xk table, confidence intervals for odds
ratio 

Lab 
The
2X2 table 

Reading 
Palta
Modules 11, 13 and 14; supplemental text 

Assignment 
NA 



Week
12 Apr
8, 10 
Lecture 
Introduction
to logistic regression, maximum likelihood
estimation 

Lab 
Analysis
of contingency tables 

Reading 
KKMN
pages 656660 and Chapter 22; Palta Modules 15 and
16 

Assignment 
NA 



Week
13 Apr
15, 17 
Lecture 
Control
of confounding with logistic regression, interaction effects in logistic
regression 

Lab 
Logistic
regression 

Reading 
KKMN
pages 660671; Palta Modules 17 and 18 

Assignment 
NA 



Week
14 
Lecture 
Logistic
regression for contingency tables 
Apr
22, 24 
Lab 
Confounding
and interaction 

Reading 
Palta
Module 19 

Assignment 
Homework
4 due on Apr 24 



Week
15 
Lecture 
Goodness
of fit of logistic regression 
Apr
29, May 1 
Lab 
Likelihood
ratio test and goodnessoffit 

Reading 
Palta
Module 20 

Assignment 
NA 



Week
16 May
6, 8 
Lecture 
Logistic
regression of casecontrol data and conditional logistic
regression 

Lab 
Review
of final exam 

Reading 
KKMN
2352; Palta Modules 21 and 22 

Assignment 
Homework
5 due on May 8 



Week
17 

In
class final exam during finals week 


