Courses & Timetables

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

james [dot] hanley [at] mcgill [dot] ca (Dr. J. Hanley) / andrea [dot] benedetti [at] mcgill [dot] ca (Dr. A. Benedetti)

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 (for Biostatistics & Math/Stat students). EPIB-607 and EPIB-621 for Epidemiology students.

Open to students in Epidemiology, Biostatistics and Math/Stat programs who have completed their first year courses. Students in other disciplines require permission of the instructor.

Academic Credits: 4

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

michal [dot] abrahamowicz [at] mcgill [dot] ca (Dr. M. Abrahamowicz)

This course in applied biostatistics aims at developing more advanced skills and insights into multivariable modeling. The main focus will be on flexible modeling of survival, i.e. time-to-event processes, in both etiologic epidemiological studies and in prognostic studies of disease progression and its treatment. We will learn how to model and interpret (i) time-dependent covariates, and (ii) associations which vary over time, i.e. time-varying effects, (iii) complex multi-dimensional time-dependent exposures, such as lifetime history of occupational exposure, , including (iv) cumulative effects. Extensions of survival analyses to (a) relative survival methods that deal with unknown causes of death, and (b) Markov multi-state modeling will be also discussed. We will also explore some methodological issues, ‘generic’ for any complex multivariable analyses, regardless of whether the outcome is continuous, binary or event time, such as flexible modeling of non-linear effects of continuous variables, as well as estimation and testing of interactions. We will introduce, at a non-technical, conceptual level, the role of computer simulations as an essential tool (e.g. to validate or assess novel methods) of modern methodological research in biostatistics and epidemiology.

Lectures will involve both (i) explanation of the underlying concepts and methodological principles, with main focus on why and when the method should be applied, and (ii) real-life epidemiological applications of these methods, with focus on both how the method was implemented and how the results should be interpreted.

The course is oriented toward 3 groups of graduate students: (i) PhD students in Epidemiology and Occupational Health streams may use some of these advanced methods in their doctoral (or future) research, (ii) methodologically-oriented MSc students in Epidemiology who enjoyed the mandatory courses in biostatistics, and are eager to learn more advanced methods, and (iii) PhD and MSc students in Biostatistics or Statistics (from Math & Stati Dept.), especially those who are interested in more applied aspects of biostatistical research.

Epidemiology students will learn to correctly interpret and critically evaluate the methods and results of such methods in modern epidemiological research, and to implement some, more user-friendly methods in their own data analyses. The Biostatistics/Statistics students, in addition, will gain some insight into the underlying theory of non-parametric modeling, and some ‘technical’ aspects of modeling and simulations.

Academic Credits: Epidemiology 3 | Biostatitistics 4

Pre-requisites:

For Epidemiology students: At Least 2 first courses in Statistics offered by the Department (EPIB 607 and EPIB 621), or equivalent, or the Instructor’s permission.

For Biostatistics students, and graduate students in Math&Stat Departments, there are no specific prerequisites but good understanding of multivariable regression and basic knowledge of survival analysis will be expected.

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

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

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

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

james [dot] hanley [at] mcgill [dot] ca (Dr. J. Hanley) / andrea [dot] benedetti [at] mcgill [dot] ca (Dr. A. Benedetti)

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 (for Biostatistics & Math/Stat students). EPIB-607 and EPIB-621 for Epidemiology students.

Open to students in Epidemiology, Biostatistics and Math/Stat programs who have completed their first year courses. Students in other disciplines require permission of the instructor.

Academic Credits: 4

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

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

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

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