Courses

BIOSTATISTICS

IMPORTANT NOTE

  • Special students and students from other departments or universities require the permission of the course instructor.
  • Course numbers BIOS 691, 692, 693, 694, 695, 696, 697, 698 and 699 belong to a group of Special Topics course numbers, and can be used only once. If you have used any of these numbers in a previous course, please contact the Student Affairs Office.
  • Several required courses and relevant elective courses for our programs are offered at the department of Mathematics and Statistics. Please visit their 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.

Fall 2018

Timetable

BIOS 601 Epidemiology: Introduction and Statistical Models

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 610 Causal Inference in Biostatistics

robert.platt [at] mcgill.ca (Dr. R. Platt)

Biostatistics : Foundations of causal inference in biostatistics. Statistical methods based on potential outcomes; propensity scores, marginal structural models, instrumental variables, structural nested models. Introduction to semiparametric theory.

Prerequisite: BIOS 602, MATH 557, or equivalent, or permission of the instructor.

This course is intended for PhD and advanced Masters students in Biostatistics or Statistics.

Academic Credits: 4

BIOS 612 Advanced Generalized Linear Models

alexandra.schmidt [at] mcgill.ca (Dr. A. Schmidt)

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 624 Data Analysis & Report Writing

andrea.benedetti [at] mcgill.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

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

BIOS 690 M.Sc. Thesis

A review, appraisal of the performance, or application of, selected biostatistical methods, carried out under supervision.

Academic Credits: 24

BIOS 702 Ph.D. Proposal - Fall & Winter

micheal.kramer [at] mcgill.ca (Dr. M. Kramer) / michal.abrahamowicz [at] mcgill.ca (Dr. M. Abrahamowicz)

The course will prepare students for their PhD thesis research protocol.  Under the active tutelage of their PhD thesis supervisor and other thesis committee members, students are expected to develop an important research question that will be addressed by epidemiologic and biostatistical methods of the highest scientific quality.  In doing so, they should acquire essential skills for writing and defending research proposals and grant applications, including the importance of the research question(s), formulation of research objectives to answer those questions, the design(s) proposed to achieve the objectives, statistical analytic strategies, and the strengths and limitations of the proposed research.  The relative emphasis of the substantive, design, and analytic compoonents will of course be adapted according the the student's PhD curriculum stream:  epidemiology or biostatistics.  The student should make his or her original contribution to the research.

The course runs over the fall and winter terms and meets every week that a presentation is scheduled.  It will not be offered during the summer months.  Students enrolled in a given academic year are expected to attend all protocol defenses by their fellow students in both semesters, regardless of the timing of their own defense.  Absences for a specific session should be approved by the course instructors.  Early in the fall semester, enrolled students will meet with the course instructors to clarify and discuss the course's goals, expectations, and procedures.

Course Outline [pdf]

MATH 533 Honours Regression and ANOVA 4 Credits
    Offered in the:
  • Fall
  • Winter
  • Summer

MATH 556 Mathematical Statistics 1 4 Credits
    Offered in the:
  • Fall
  • Winter
  • Summer

Winter 2018

Timetable

BIOS 602 Epidemiology: Regression Models

erica.moodie [at] mcgill.ca (Dr. E. Moodie)

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 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

BIOS 637 Advanced Modeling: Survival and Other Multivariable Data

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

Advanced applied biostatistics course dealing with flexible modeling of non-linear effects of continuous covariates in multivariable analyses, and survival data, including e.g. time-varying covariates and time-dependent or cumulative effects. Focus on the concepts, limitations and advantages of specific methods, and interpretation of their results. In addition to 3 hours of weekly lectures, shared with epidemiology students, an additional hour/week focuses on statistical inference and complex simulation methods. Students get hands-on experience in designing and implementing simulations for survival analyses, through individual term projects.

Academic Credits: 4

Pre-requisites: Graduate students in Biostatistics or Math/Stat programs, or permission of the instructor. Students are expected to have a good understanding of multivariable regression and basic knowledge of survival analysis.

BIOS 690 M.Sc. Thesis

A review, appraisal of the performance, or application of, selected biostatistical methods, carried out under supervision.

Academic Credits: 24

BIOS 693 Spatial and spatio-temporal epidemiology
(Special Topics Course)

alexandra.schmidt [at] mcgill.ca (Dr. A. Schmidt)

This course will discuss models for spatial and spatio-temporal processes, focusing on geostatistics, Markov random fields, point processes, and dynamic linear models for the analysis of time series. Tentative topics include:

  • Geostatistics: stationary random fields; variogram / covariance function; kriging; isotropic and anisotropic models;
  • Markov random fields: conditionally specified Gaussian and binary fields; simulation; parameter estimation; image analysis; disease mapping;
  • Point processes: Poisson processes; stationary point processes; tests for complete spatial randomness; other models of point processes;
  • Dynamic linear models (DLM): introduction, trend and seasonal models, dynamic regression, inference in DLM's;
  • Spatio-temporal processes.

Most of the inference procedure will follow the Bayesian paradigm, and R packages will be used to analyze real datasets.

Prerequisite(s): MATH 523, MATH 533, MATH 556 and MATH 557

Academic Credits: 4

BIOS 697/695 Prediction Modeling
(Special Topics Course)

paramita.chaudhuri [at] mcgill.ca (Dr. P. Saha Chaudhuri)

This course will provide introduction to methods and application of prediction modeling for analysis of healthcare, clinical and epidemiological data. We will learn steps for developing prediction model for various types of outcomes with particular focus on binary and survival outcomes. We will also learn about methods to assess the accuracy of prediction models and pitfalls to avoid. Implementation will focus on use of appropriate statistical packages in statistical programming language R.

Prerequisite: Permission of instructor.

Academic Credits: 3 (Students in Biostatistics programs MUST register for both BIOS 697 and BIOS 695.)

BIOS 702 Ph.D. Proposal - Fall & Winter

arijit.nandi [at] mcgill.ca (Dr. A. Nandi) / michal.abrahamowicz [at] mcgill.ca (Dr. M. Abrahamowicz)

The course will prepare students for their PhD thesis research protocol.  Under the active tutelage of their PhD thesis supervisor and other thesis committee members, students are expected to develop an important research question that will be addressed by epidemiologic and biostatistical methods of the highest scientific quality.  In doing so, they should acquire essential skills for writing and defending research proposals and grant applications, including the importance of the research question(s), formulation of research objectives to answer those questions, the design(s) proposed to achieve the objectives, statistical analytic strategies, and the strengths and limitations of the proposed research.  The relative emphasis of the substantive, design, and analytic compoonents will of course be adapted according the the student's PhD curriculum stream:  epidemiology or biostatistics.  The student should make his or her original contribution to the research.

The course runs over the fall and winter terms and meets every week that a presentation is scheduled. It will not be offered during the summer months. Students enrolled in a given academic year are expected to attend all protocol defenses by their fellow students in both semesters, regardless of the timing of their own defense. Absences for a specific session should be approved by the course instructors. Early in the fall semester, enrolled students will meet with the course instructors to clarify and discuss the course's goals, expectations, and procedures.

Course Outline [pdf]

MATH 523 Generalized Linear Models 4 Credits
    Offered in the:
  • Fall
  • Winter
  • Summer

MATH 557 Mathematical Statistics 2 4 Credits
    Offered in the:
  • Fall
  • Winter
  • Summer

Please click here to see the Summer course offerings.