BIOSTATISTICS COURSES OFFERED
IMPORTANT
- Special students and students from other departments or universities require the permission of the course instructor.
- Several required courses and relevant elective courses for our programs are offered at the department of Mathematics and Statistics. Please visit the website for further details on these courses.
- Elective courses may also be taken from other universities in Montreal. Please see the ISM listings for courses open to McGill students.
- Courses from other departments may also be appropriate; see, for example, PSYC 541.
TIMETABLES
- Fall 2012 [pdf]
- Winter_2013 [pdf]
COURSES OFFERED
- FALL: BIOS 601 MATH 533 MATH 556
- WINTER: BIOS 602 BIOS 612 BIOS 624 BIOS 692 MATH 523 MATH 557
- Comprehensives and Proposal: BIOS 700 BIOS 701 BIOS 702
COURSE DESCRIPTIONS
BIOS 601 Epidemiology: Introduction and Statistical Models
james [dot] hanley [at] mcgill [dot] ca (Dr. J. Hanley)
Examples of applications of statistics and probability in epidemiologic research. Sources of epidemiologic data (surveys, experimental and non-experimental studies). Elementary data analysis for single and comparative epidemiologic parameters.
Prerequisite: undergraduate course in mathematical statistics at level of MATH 324 or permission of instructor.
Academic Credits: 4
BIOS 602 Epidemiology: Regression Models
olli [dot] saarela [at] mail [dot] mcgill [dot] ca (Dr. O. Saarela)
Multivariable regression models for proportions, rates, and their differences/ratios; Conditional logistic regression; Proportional hazards and other parametric/semi-parametric models; unmatched, nested, and self-matched case-control studies; links to Cox’s method; Rate ratio estimation when “time-dependent” membership in contrasted categories.
Prerequisite: MATH 556 and BIOS 601 or their equivalents or permission of instructor.
Academic Credits: 4
BIOS 612 Advanced Generalized Linear Models
erica [dot] moodie [at] mcgill [dot] ca (Dr. E. Moodie)
Statistical methods for multinomial outcomes, overdispersion, and continuous and categorical correlated data; approaches to inference (estimating equations, likelihood-based methods, semi-parametric methods); analysis of longitudinal data; theoretical content and applications.
Prerequisite: MATH 523 and MATH 533 or their equivalents or permission of instructor.
Open to students in Biostatistics and Math/Stat programs. Students in other disciplines require permission of the instructor.
Academic Credits: 4
BIOS 613 Introduction to Statistical Genetics
aurelie [dot] labbe [at] mcgill [dot] ca (Dr. A. Labbe)
Offered in Summer 2013
Introduction to genetic epidemiology. Linkage analysis (parametric and non-parametric). Quantitative trait analysis. Linkage disequilibrium. Association analysis (candidate gene and genomewide). eQTL studies.
Prerequisite: Permission of instructor. Undergraduate course in mathematical statistics at level of MATH 324.
Academic Credits: 4
BIOS 624 Data Analysis & Report Writing
robert [dot] platt [at] mcgill [dot] ca (Dr. R. Platt)
Common data-analytic problems. Practical approaches to complex data. Graphical and tabular presentation of results. Writing reports for scientific journals, research collaborators, consulting clients.
Prerequisite: MATH 523 and MATH 533 or their equivalents or permission of instructor.
Open to students in Biostatistics and Math/Stat programs. Students in other disciplines require permission of the instructor.
Academic Credits: 4
BIOS 630 Research Project/Practicum in Biostatistics
Critical appraisal of the biostatistical literature related to a specific statistical methodology. Topic to be approved by faculty member who will direct student and evaluate the paper. Projects will be carried out within a course framework, with a common start/end date for all students.
Academic Credits: 6
A review, appraisal of the performance, or application of, selected biostatistical methods, carried out under supervision.
Academic Credits: 24
BIOS 692 Unsupervised Learning with biomedical applications
Special Topics in Biostatistics
antonio [dot] ciampi [at] mcgill [dot] ca (Dr. A. Ciampi)
This course has roots in both machine learning (unsupervised learning) and modern statistical data exploration (data reduction, clustering, and mixtures of distributions). Theory, methods and applications to Biostatistics and other biomedical sciences will be discussed. Topics will include:
- Review of basic Principal Component Analysis and Clustering from a Multivariate Analysis perspective
- Introduction to unsupervised learning with emphasis on the statistical perspective Model choice
- Advanced data reduction techniques and Clustering
- Kohonen maps and other neural architectures for clustering
- Model based Clustering
Prerequisites: EPIB 697 or Math/Stat 423 & 523, or equivalent. Open to students from Biostat and Stat, but also to students from other disciplines such as Computer Science, Bioinformatics and Engineering provided they have an appropriate math and stat background, roughly equivalent to the prerequisites.
Academic Credits: 4
BIOS 700 Ph.D. Comprehensive Exam, Part A
BIOS 701 Ph.D. Comprehensive Exam, Part B
erica [dot] moodie [at] mcgill [dot] ca (Dr. E. Moodie)
The comprehensive exam is given in two parts. The objective is to assess the degree to which students have been able to assimilate and apply statistical theory and methods for biostatistics. BIOS 700 (written exam) is held twice yearly and addresses statistical theory. BIOS 701 (take-home exam) is held once yearly and addresses applied biostatistics.
For additional information see:
BIOS700CompExam [.pdf]
BIOS701CompExam [.pdf]
Academic Credits: 0
BIOS 702 Ph.D. Proposal
james [dot] brophy [at] mcgill [dot] ca (Dr. J. Brophy) / antonio [dot] ciampi [at] mcgill [dot] ca (Dr. A. Ciampi)
The course will prepare students for their doctoral thesis research. Students will acquire essential skills for writing and defense of research objectives and methods. This course is cross-listed with EPIB 702.
Students will normally take this course in their second year of study, following the completion of BIOS 700 (PhD Theory Comprehensive Examination). Students are expected, under the active tutelage of their supervisors and thesis committee members, to have developed a scientifically appropriate research question that will be addressed by rigorous research methods of the highest quality. Students need to demonstrate essential grantsmanship skills in both writing and defending their research problem. While the exact methods of achieving the goals are often not easily described in initial stages of biostatistics research, the student will be expected to describe a well-defined problem, perform a thorough review of the relevant literature, and provide an outline of a proposed solution(s) to the problem.
The course is run over the Fall and Winter terms. It will not be offered during the summer months. The course will meet every week that a presentation is scheduled. It is expected that all students enrolled in a given academic year will attend all presentations by their fellow students in both semesters (i.e. presentations by students in both the Biostatistics and the Epidemiology programs), regardless of the timing of their own protocol defenses. In the first week of the Fall semester, the students will meet with the course instructors to discuss the goals, expectations, and procedures.
For additional information see:
BIOS702Protocol [pdf]
Academic Credits: 0