Course Description: BIOS601: Epidemiology, Introduction & Statistical models: Fall 2022

[updated August 10, 2022]

Instructor Dr. James Hanley
Co-ordinates tel: (514) 398-6270  
e-mail: james.hanley@mcgill.ca
Web-page: http://www.biostat.mcgill.ca/hanley
Office: Room 1214, 12th floor, 2001 McGill College Avenue.
Overview The course covers the elementary statistical principles and methods used in data-analysis in scientific and population-health research, and the theoretical (mathematical-statistics) foundations for these methods. The applications illustrated will be drawn mainly from epidemiologic and clinical research; portions of the classes and assignments will involve mathematical statistics; others will require some statistical computing.
Target This course is aimed at MSc and PhD students in the department's biostatistics program, i.e., students with undergraduate training in mathematical statistics who are interested in the application of statistical methods to epidemiologic, biomedical and other biological research. However the skills and understanding acquired in this course are also applicable to other fields of research that use quantitative methods. Statistically-prepared students from other departments or universities are especially welcome.
Course Website http://www.biostat.mcgill.ca/hanley/bios601/index.html
Topics 1a: Epidemiologic concepts, terminology and measures.
1b: Surveys, sampling, and measurement
1c: Statistical models, inference and planning for such (1-sample) studies.

2a: Comparative studies [experimental, non-experimental, quasi-experimental]
    -- in general
    -- specifically in epidemiology (with associated terminology and measures).
2b: Statistical models, inference and planning for comparative studies.

3: Study designs & statistical methods for reducing bias & increasing precision.
Approach

It is assumed that students will have already encountered the basic statistical models (Gaussian, Binomial and Poisson random variables) and basic inferential procedures. The distinguishing features will be the
• use of likelihood as a unifying theme
• integration of study design, data analysis models, and precison/power/sample size
• coverage of both larger- and smaller-sample situations
• focus on a minimalist approach, and on concepts, so that one can more easily go from a familiar to an unfamiliar statistical problem
• reference to (generic) classic texts by Cox (Planning of Experiments), Cochran (Observational Studies), Campbell and Stanley (Quasi-experimental Designs), and the specific one by Clayton and Hill (Statistical Models for Epidemiology) which uses likelihood as a unifying theme.

When Mondays and Wednesdays 08:30-10:30.     first/last class: Aug 31/Nov 30.
Where In-person, 2001 McGill College Avenue, 11th floor, Room 1135
Prerequisites Undergraduate course in mathematical statistics at level of MATH 324, or permission of instructor.
No. of Credits 4
Assessment To be discussed at first class: suggested:- assignments [xx%], participation [xx%], exams[xx%]
 
Academic
Integrity
McGill University Senate resolution of January 29, 2003 on academic integrity...

McGill University values academic integrity. Therefore all students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct and Disciplinary Procedures. For more details, consult the link below.

L'université McGill attache une haute importance à l’honnêteté académique. Il incombe par conséquent à tous les étudiants de comprendre ce que l'on entend par tricherie, plagiat et autres infractions académiques, ainsi que les conséquences que peuvent avoir de telles actions, selon le Code de conduite de l'étudiant et des procédures disciplinaires (pour de plus amples renseignements, veuillez consulter le site
 
http://www.mcgill.ca/students/srr/honest/