Course outline / EPIB-621 Data Analysis in the Health Sciences

Lawrence Joseph

Bayesian statistics

Professor
E-mail: lawrence.joseph@mcgill.ca
Telephone: +1 (514) 934-1934 ext: 44713
FAX: (514) 934-8293
Complete CV

Division of Clinical Epidemiology
McGill University Health Centre
Royal Victoria Hospital
687 Pine Avenue West
V Building, Room V2.10
Montreal, Quebec
Canada, H3A 1A1

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Courses
EPIB-621 Data Analysis in the Health Sciences
Course outline

Courses taught

EPIB-607 Principles of Inferential Statistics in Medicine

EPIB-613 Introduction to Statistical Software

EPIB-621 Data Analysis in the Health Sciences

EPIB-651 Bayesian Analysis in Medicine

EPIB-675 Bayesian Analysis in the Health Sciences

Publications

Methodological publications

Bayesian Sample Size

Change-point methods and applications

Diagnostic testing

Meta analysis

Other topics

Medical publications

Allergy and immunology

Bayesian software

Change-point methods and applications

Diagnostic testing

Diagnostic testing in Genetics

Links

EPIB-621 Data Analysis in the Health Sciences

Course Outline

1	Mon Jan	9	Introduction/Motivation/Evaluation/Scope/Background Lecture given by Aurélie
			Review of Hosmer D and Lemeshow S: Applied Logistic Regresison Review of Woodworth GG: Biostatistics: A Bayesian Introduction Review of Gelman A, Carlin J, Stern H,and Rubin H. Bayesian Data Analysis
2	Wed Jan	11	Frequentist inferences for means and proportions Lecture given by Aurélie
			Probability and Sampling Inference for means Inference for proportions
3	Mon Jan	16	Bayesian inferences for means and proportions Lecture given by Lawrence
4	Wed Jan	18	Introduction to R Lecture given by Aurélie
			R program for Bayesian normal means with known variance
5	Mon Jan	23	Simple linear regression: one variable Lecture given by Aurélie
			R program for simple regression with CIs Data for DMF teeth example
6	Wed Jan	25	Linear regression with two or more variables: basic concepts Lecture given by Aurélie
			R program for multiple regression with CIs HeartData.txt
7	Mon Jan	30	Dummy variables in linear regression Lecture given by Aurélie
			Smoking data
8	Wed Feb	1	Confounding and collinearity in linear regression Lecture given by Aurélie
			hr.txt Aurelie's extra notes on confounding
9	Mon Feb	6	Interaction terms and prediction in linear regression Lecture given by Aurélie
			fat.txt Aurelie's extra notes on centering Aurelie's extra notes on interactions
10	Wed Feb	8	Goodness of fit in linear regression Lecture given by Aurélie
11	Mon Feb	13	Bayesian inference for linear regression models Lecture given by Lawrence
			WinBUGS Quick Reference for Model Preparation WinBUGS Quick Reference for Analysis WinBUGS program for Binomial Proportion WinBUGS program for Binomial Proportion Difference WinBUGS program for Normal Mean, Known Variance WinBUGS program for Normal Mean, Unknown Variance WinBUGS program for Linear Regression WinBUGS program for Logistic Regression WinBUGS program for Hierarchical Binomial Proportion
12	Wed Feb	15	Model selection and predictions in linear regression Lecture given by Lawrence
			Model Selection article by Adrian Raftery Article comparing AIC to the BIC (and programming in SAS)
13	Mon Feb	20	No Class -- Spring Break
14	Wed Feb	22	No Class -- Spring Break
15	Mon Feb	27	Review Lecture given by Aurélie
16	Wed Feb	29	Midterm Exam
17	Mon Mar	5	Hierarchical/random effects linear models Lecture given by Lawrence
			CRP Data Set Formatted for R CRP Data Set Formatted for WinBUGS Blood Pressure Data Set with WinBUGS Program
18	Wed Mar	7	Introduction to logistic regression: Univariate Lecture given by Aurélie
			Age and CHD Data Set in R Format R program for logistic regression with CIs and ORs
19	Mon Mar	12	Introduction to logistic regression: Multivariate Lecture given by Aurélie
			ICU Data Set in R Format
20	Wed Mar	14	Confounding and collinearity in logistic regression Lecture given by Aurélie
			Low birth weight data
21	Mon Mar	19	Goodness of fit in logistic regression Lecture given by Aurélie
22	Wed Mar	21	Bayesian analysis of logistic regression models Lecture given by Lawrence
			Low birth weight data in WinBUGS format
23	Mon Mar	26	Model selection in logistic regression Lecture given by Lawrence
24	Wed Mar	28	Hierarchical/random effects logistic regression Lecture given by Lawrence
			WinBUGS program and data for osteoporosis example Article by James Brophy illustrating multi-level hierarchical models BUGS program by James Brophy for multi-level hierarchical models
25	Mon Apr	2	Missing data in regression models Part 1 Lecture given by Lawrence
			Simulated CRP Data Set Kmetic et al: Example of how to handle non-ignorable missing data
26	Wed Apr	4	Missing data in regression models Part 2 Lecture given by Lawrence
27	Mon Apr	9	No Class - Easter Monday
28	Wed Apr	11	Measurement error Lecture given by Lawrence
29	Mon Apr	16	Review Lecture given by Aurélie
30	Wed Apr	18	Final Exam (Date to be confirmed)