Preliminary plan
Notes, Data sets and programs
for
Postgraduate course
in
Linear and logistic regression

Edited December 9th, 2004
By Morten Frydenberg
morten@biostat.au.dk


Day 1: Monday November 22 2004
9.15 - 10.30 Lecture: Simple linear regression -1 .
The model, the parameters, estimation and inference.

All STATA code used at the lecture.
Data set used at the lecture STATA: lung.dta . SPSS: lung.sav.
10.30 - 12.00 Exercises .
The lung data STATA lung.dta . SPSS: lung.sav.
12.00 - 13.00 Lunch break
13.00 - 14.30 Lecture: Simple linear regression -2 .
Checking the model, residuals, leverage, diagnostics plots,transformation of variables.

Most of STATA code used at the lecture.
Data set used at the lecture: STATA: lung.dta and gfrdata.dta . SPSS: lung.sav and gfrdata.sav .
14.30 - 16.00 Exercises .
The gfr data STATA gfrdata.dta SPSS: gfrdata.sav .
The glyco data STATA glyco.dta SPSS: glyco.sav .


Day 2: Thursday November 25 2004

9.15 - 9.30 Summarizing Mondays exercises.
9.30 - 10.30 Lecture: Multiple linear regression - 1 .
The model, the parameters, estimation and inference.
Checking the model.

All of STATA code used at the lectures.
Data set used at the lecture STATA: fram200.dta . SPSS: fram200.sav .
10.30 - 12.00 Exercises .
Data STATA: lung.dta and fram200.dta . SPSS: lung.sav and fram200.sav .
12.00 - 13.00 Lunch break
13.00 - 14.30 Lecture: Multiple linear regression - 2 .
Working med categorical explanatory variables
Interaction/effectmodification.
14.30 - 16.00 Exercises .
Data STATA: lung.dta and fram200.dta . SPSS: lung.sav and fram200.sav .

Home work
The home work with data sets STATA: case_control.dta and serumchol.dta . SPSS: case_control.sav and serumchol.sav .
Slides used at the discussion og the home work.


Day 3: Monday December 6 2004

9.15 - 10.00 Summarizing the home work exercises.
10:15 - 12:00 Lecture: Logistic regression .
Odds ratios via logistic regression
Continuous independendt variables
Categorical independendt variables
Interactions
Wald and likelihood ratio test
The logistic regression model in general

Most of STATA code used at the lectures.
Data set used at the lecture STATA: obese.dta and case_control.dta . SPSS: case_control.sav and obese.sav.
12.00 - 13.00 Lunch break.
13.00 - 14.30 The lecture continued.
14.30 - 16.00 Exercises .
The prostate cancer data set prossub.dta . SPSS: prossub.sav .



Day 4: Thursday December 9 2004

9.15 - 10.00 Exercises. - Monday afternoon continued
10.15 - 12.00 Lecture: Working with linear and logistics regression models .
Diagnostics for logistic regression
Test and estimation after the model has been fitted in STATA
Colinearity
Things to consider when specifying a model
Model selection an its consequences

All STATA code used at the lecture.
Data set used at the lecture STATA: obese.dta and serumchol194.dta .
SPSS: obese.sav and serumchol194.sav .
12.00 - 13.00 Lunch break
13.00 - 15.00 Lecture: Extensions .
Conditional logistic regression
Models for relative risk or risk differences
Clustered data
Non-linear regression

All STATA code used at the lecture.
Data set used at the lecture STATA:
obese.dta , oralcancer.dta , FEV.dta and AZT.dta .
SPSS:
obese.sav , oralcancer.sav , FEV.sav and AZT.sav .
15.15 - 16.00 Course evaluation

Lecture notes with 4 slides per page
Day 1 morning and Day 1 afternoon
Day 2 morning and Day 2 afternoon
Day 3
Day 4 morning and Day 4 afternoon

The examination
The assignment and the data STATA: ExamE2004.dta. SPSS: ExamE2004.sav.

Participants who are taking the course as the final part of the mandatory Ph.D. course in biostatistics
are required to hand in a solution to an assigment that is given at the end of the course.
The assigment has the form of a statistical analysis of a data set and the participants must return an individual solution in form of a short paper before

Wednesday January 5 2005 at 12 a.m. at the Department of Biostatistics.
To pass the examination the paper should give a satisfactory analysis of the data and answer the questions posed in the assigment.
Note, the assigment will also evaluate the Basic course in Biostatistics. .