Poisson regression 2017

Hi,

These are materials for Poisson regression course, academic year 2017 (2018). This year I use R version 3.4.3. The results are similar to those obtained from the previous versions of R.

montana.dat
montana.dta
docsmoke.dat
docsmoke.dta
welsh.dat
welsh.dta

The functions for Poisson regression are within ice package.

The module can be downloaded here.
Poisson1702.pdf

You can also download ICE modules from ice and epid. However, they are not necessary for this session.

Finally, you can follow my R script file below. Don't forget to change working directory to yours.

poisson.R

Logistic regression II, Conditional logistic regression, 2017

Hi,

These are materials for logistic regression course II: Conditional logistic regression, academic year 2016 (2017). This year I use R version 3.4.3 and the regression outputs are the same as that produced from the previous version of R.

Conditional logistic regression requires library survival that already exists on your R library folder. Just call library(survival) or require(survival) and you are ready to use clogit function.

agechd.dta
cca-match.dta

The module can be downloaded here.

logistic1702-2.pdf

The required modules for this course are ice and epid which can be installed into R by typing the following two lines on your R console.

install.packages("ice", repos="http://r-ice-project.org")
install.packages("epid", repos="http://r-ice-project.org")

In this session, we are going to use multilevel logistic regression (glmer) in dealing with data stratified by age group as if the stratification is a matching condition. This method is recently used by some authors. Then we need another package from CRAN called "lme4". You can install the package by typing the following line on your R console.

install.packages("lme4")

(Since the package is installed from CRAN or its mirror, a repos argument is not required.)

Finally, you can follow my R script file below. Don't forget to change working directory to yours.

conditional2.R

Additional reading:
The article "Medication risk factors associated with healthcare-associated Clostridium difficile infection: a multilevel model case–control study among 64 US academic medical centres" shows how to use multilevel logistic regression so that the effect of the variables at the contexual level can be identified. This method can be used to handle the matched analysis of a case-control study as well.

J. Antimicrob. Chemother.-2014-Pakyz-1127-31.pdf

Exercise:
Try analyzing the following data. It is the data set of a matched case-control study. The matching variables are sex, age, and alcohol drinking and smoking habits. Explore the case:control ratio of the matched sets and determine how to analyze to see the effects of the following genes on cancer status: gstm1, gstt1, p53,  cyp2E1, mEH3, mEH4, and MPO. This is just an exercise, so don't worry about the action of these genes on cancer, alcohol drinking and cigarette smoking.

oralex.dta
lowbwtm11.dta 

Logistic regression I, 2017

Hi,

These are materials for logistic regression course I, academic year 2017 (2018). This year I use R version 3.4.3. The outputs of R are the same as with the previous versions of R.

For the exercises in the module, download the following files.
anc.dta
agechd.dta
cca.dta
lowbwt.dta

The revised module can be downloaded here.
logistic1702-1.pdf

The required modules for this course are ice and epid which can be installed into R by typing the following two lines on your R console.

install.packages("ice", repos = "http://r-ice-project.org")
install.packages("epid", repos = "http://r-ice-project.org")



Finally, you can try following my R script file. Please change the working directory to yours, otherwise the script will not run correctly.
exercises.R
exercise-additional.R