Hi,
These are materials for logistic regression course I, academic year 2018 (calendar year 2019). This year, I use R version 3.5.2. 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 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 downloading "ice_3.4.0.1.zip" and "epid_3.4.0.1.zip" and install them manually into R.
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
Sunday, February 24, 2019
Saturday, February 24, 2018
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
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
Thursday, February 22, 2018
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
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
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
Thursday, November 2, 2017
Cancer Survival Analysis
Day 1 (November 6, 2017)
Concept of time to event analysis
Descriptive analysis (Life table, Kaplan Meier, Median survival, Log rank)
Regression: semi-parametric vs parametric
Cancer survival analysis: clinical trial data, hospital-based data, population-based data
Core measures of cancer surveillance (Incidence, Survival, Mortality)
Bias (lead time, length time, overdiagnosis)
Population-based cancer survival: importance and use
Day 2 (November 7, 2017)
Overall survival
Cancer prognosis (remove competing causes of death): relative survival, cause-specific survival
Actual prognosis (include competing causes of death)
Relative survival ratio and Net survival: Ederer I, Ederer II, Hakulinen, Pohar Perme
Age-standardized relative survival (Standard cancer patient population)
Cohort vs Period vs Complete approach
Practice with given a data set
Practicum in relative survival analysis
Practicum in risk models for survival
Workshop materials
Concept of time to event analysis
Descriptive analysis (Life table, Kaplan Meier, Median survival, Log rank)
Regression: semi-parametric vs parametric
Cancer survival analysis: clinical trial data, hospital-based data, population-based data
Core measures of cancer surveillance (Incidence, Survival, Mortality)
Bias (lead time, length time, overdiagnosis)
Population-based cancer survival: importance and use
Day 2 (November 7, 2017)
Overall survival
Cancer prognosis (remove competing causes of death): relative survival, cause-specific survival
Actual prognosis (include competing causes of death)
Relative survival ratio and Net survival: Ederer I, Ederer II, Hakulinen, Pohar Perme
Age-standardized relative survival (Standard cancer patient population)
Cohort vs Period vs Complete approach
Practice with given a data set
Practicum in relative survival analysis
Practicum in risk models for survival
Workshop materials
Sunday, October 29, 2017
Cancer Incidence Trends Analysis
Day 1 (October 30, 2017)
Joinpoint and age-period-cohort model
Over-all trend (APC)
Age, period, cohort effects
Population denominators – past and future
Making a data frame for joinpoint and APC analyses
Merging incidence and population dataframes
(lecture and practicum)
Day 2 (October 31, 2017)
Steps in APC tutorials – trend analysis
(lecture and practicum)
Selection of the best model – AP, AC, AP-C, AC-P, APC
Interpretation of age, period, cohort effects
(lecture and practicum)
Day 3 (November 1, 2017)
Steps in joinpoint and APC projection
Cut trend methods
(lecture and practicum)
Interpretation of the projection
Limitation of use of trend projection
Files used in the workshop for 'Cancer Incidence Trends Analysis'. They are arranged in the following folders.
Joinpoint and age-period-cohort model
Over-all trend (APC)
Age, period, cohort effects
Population denominators – past and future
Making a data frame for joinpoint and APC analyses
Merging incidence and population dataframes
(lecture and practicum)
Day 2 (October 31, 2017)
Steps in APC tutorials – trend analysis
(lecture and practicum)
Selection of the best model – AP, AC, AP-C, AC-P, APC
Interpretation of age, period, cohort effects
(lecture and practicum)
Day 3 (November 1, 2017)
Steps in joinpoint and APC projection
Cut trend methods
(lecture and practicum)
Interpretation of the projection
Limitation of use of trend projection
Files used in the workshop for 'Cancer Incidence Trends Analysis'. They are arranged in the following folders.
Tuesday, September 5, 2017
Proposal writing
Files used in the workshop for 'proposal writing'.
Drafting a Proposal
Vancouver Condensed Guide
CI5vol10
International Rules for Multiple Primary Cancers
Good Clinical Practice-Hutcha Sriplung
Curriculum Vitae-Hutcha Sriplung
Journals and Instruction to Authors
Introduction
Methods, Statistical Analysis, and Results
Software for sample size calculation
GPower3Windows.zip
GPowerMac_3.1.9.3.zip
Drafting a Proposal
Vancouver Condensed Guide
CI5vol10
International Rules for Multiple Primary Cancers
Good Clinical Practice-Hutcha Sriplung
Curriculum Vitae-Hutcha Sriplung
Journals and Instruction to Authors
Introduction
Methods, Statistical Analysis, and Results
Software for sample size calculation
GPower3Windows.zip
GPowerMac_3.1.9.3.zip
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