This is a survey course on the philosophy, theory, and practice of community-based research (CBR) in public health that has an explicit social justice agenda. The course focuses on key aspects of community based research such as anti-oppressive practices, ethics, intersectionality and inclusion in research methods and activities, CBR with marginalized communities, quantitative participatory research, the practice of CBR proposal writing, arts-based CBR, and Knowledge Mobilization (KMb) in CBR.

The purpose of this course is to prepare graduate students for the collection of quantitative and generation of qualitative data for research and evaluation projects. Using interactive weekly sessions, students will learn how to choose between and implement a variety of methods. Students will also learn how to align their projects to social justice-oriented agendas, assess issues of representation and positionality, and use community-based research approaches. For each method, we will examine common challenges and mistakes, and threats to validity, trustworthiness and fidelity. We will focus on the most used and a few novel methods of data collection and generation.

This course is an introduction to statistical techniques and reasoning and will focus on both the theory and practice of biostatistics as it applies to epidemiology; descriptive and graphical methods, estimation, tests of hypotheses, applied to both means and proportions, in both paired and independent samples; simple linear regression, introduction to analysis of variance.

The goal of this course is to continue development and application of skills in statistical analysis required in epidemiologic and health care research. Students will acquire an understanding and practice in fitting and interpreting linear regression models, logistic regression models and survival analysis models including the use of regression splines to relax assumptions of linear relationships; gain an understanding of model validation, variable selection methods and their implications for model validity and be able to use bootstrapping or cross-validation to perform internal validation; and understand the basic classifications of missing data and learn some approaches to deal with it.

The course provides fundamental public health research skills for both quantitative and qualitative researchers including health professionals to work in both research and applied settings. It focuses on practical issues involved in the design and conduct of surveys in health and social science research. Sessions will focus on acquiring the skills and knowledges to build a survey including articulating objective(s) and research question(s) as well as writing a research protocol to investigate the topic of interest on which the survey is based. This course will not provide students with basic statistical analysis skills and it is highly recommended that students have completed undergraduate statistics courses.

This unique course involves a weekly two-hour lecture and a practicum component of four hours per week. Lectures focus on design issues in term one and analysis issues in term two. The practicum involves delving into one or more projects under the direct supervision of a Division of Biostatistics faculty-appointed biostatistician. Emphasis is placed on the analysis of data and interpretation of results. Student evaluation is based on end-of-term presentations, progress in the practicum component, and an end-of-year final report.

This unique course involves a weekly two-hour lecture and a practicum component of four hours per week. Lectures focus on design issues in term one and analysis issues in term two. The practicum involves working on one or more PhD-level projects, under the direct supervision of a Division of Biostatistics faculty-appointed biostatistician. Analysis of data and interpretation of results are emphasized. Student evaluation is based on end-of-term presentations, progress in the practicum component, and an end-of-year final report.

The aim of this course is to get familiar with the most important concepts and methods in analysis of censored survival data, with the main emphasis in regression modelling. After taking the course, the students are able to apply and understand the most important non- parametric, semi-parametric and parametric approaches to analyzing survival data. The students are able to choose, check, and interpret an appropriate model to answer a specific question, with a contrast being made between models developed for investigating covariate effects, and models developed for prediction purposes.

This course is designed to provide students with an understanding of the statistical methods used in categorical data analysis. These include traditional methods for two-way contingency tables (e.g., Chi-squared test, Fisher's exact test). The majority of the course, however, will focus on regression models, with a particular emphasis on logistic regression models. Analysis of repeated categorical response data, namely marginal and random effects models will also be covered. Inference using maximum likelihood estimation will be emphasized. Both application and theory will be covered.

This course will introduce students to methods and approaches for predictive modelling and teach the associated technical knowledge and computational approaches required to successfully implement an applied analysis. The material covered will include the general approaches to predictive modelling and a selection of methods that would typically be used at various stages of a pipeline when training a model. This includes handling of missing data, variable selection, and fitting a final predictive model. The approach of the course will be to familiarize the students with the methods and technicals via applications, with a focus on data that is commonly encountered in the health sciences.

This course covers statistical methods for analysis of microbiome and ecological data. The focus will be on familiarizing students with the methods commonly used to analyse microbiome data as well as some methods from statistical ecology from which these methods originated. Emphasis will be on understanding why microbiome data is different from commonly encountered medical data and how the methods developed have attempted to address certain issues. A focus will be placed on understanding the current methods available and when they are applicable, particularly differentiating the two main approaches for data analysis (univariate vs. multi-outcome). As research in these methods is ongoing, current trends in research will be covered.

This course presents an introduction to epidemiologic concepts and the application of quantitative methods. Topics include measurement of disease occurrence, descriptive epidemiology, ecologic studies, cohort studies, case-control studies, measurement validity, screening, causation, random variation, bias, confounding, effect modification, randomized controlled trials, and epidemic investigation. The course utilizes a wide variety of case studies from both chronic and infectious disease epidemiology, representing both landmark studies and newer research.

The course introduces statistical methods to analyze correlated data commonly encountered in health science research. Topics will include: data visualization, linear models for correlated data, linear mixed-effects models, marginal models, generalized linear mixed-effects models, multilevel models, missing data, and drop-out. Examples are extensively used to illustrate concepts and implemented using the software R.

In the last 30 years, Bayesian methods have become an important tool for applied statisticians, biostatisticians, and data scientist. Bayesian thinking allows one to quantify uncertainty and is a method which allows one to learn from new data. It is extremely flexible. The reason for its recent popularity is not only because of advances in physical computer power but also advances in the fundamental algorithms used for Bayesian problems. This course will first explain the basics of Bayesian inference and Markov chain Monte Carlo methods. From there, this course will show how to compute and make inferences on complex data problems using these methods.

This course covers the fundamental statistical problems in genetics, with an emphasis on human genetics. The major topics to be covered include: introduction to molecular genetics and overview of major research area of statistical genetics, principles of population genetics, segregation analysis, genetic map, parametric and nonparametric linkage analysis, association studies, special topics, and computing labs.

Rigorously designed clinical trials are essential for the clinical development of novel therapies and interventions. This course will provide students with knowledge of advanced statistical methods required for the design and analysis of clinical trials across all phases of clinical development. The course will teach both traditional and complex trial designs, including trials with adaptive designs. Students will learn how to evaluate the operating characteristics of designs using statistical simulations and make informed choices in selecting an appropriate design for the trial. Students will also learn about the role of a trial statistician including protocol writing, interim monitoring, analysis, and reporting.

The overall purpose of this course is to provide the mathematical foundations for the techniques is Biostatistics. A master's-level biostatistician needs to read and understand the statistical/biostatistical literature. This is necessary to not only understand the strength and weaknesses of present statistical methods but to also be able to understand and apply future techniques that will be developed during ones career. Most likely, students would have been exposed to the basics of statistical inference in more applied courses where one is scanning through some SAS code looking for the p-values. Here we will reintroduce these concepts with a view to the mathematical underpinnings. Since this is mathematics, it will be necessary to understand the basic concepts of a mathematical proof. Students should have a good background in applied statistics, and it would be helpful to be somewhat familiar with the basic concepts in univariate calculus.

The fundamentals of planning and analysis of clinical trials are an integral part of biostatistician's training. At the end of the course, the students should be able to critically evaluate plans and designs for clinical trials, participate in protocol writing, help to formulate a primary research question/hypothesis that can be statistically tested in a trial, help to specify appropriate intervention and control groups and outcomes, use available design and analysis options to reduce bias and variability of the results, and determine the size of a trial to obtain appropriate power to detect differences. The students will also have familiarity with the statistical aspects of the CIHR RCT evaluation criteria and structure.

This course is a one-hour Journal Club/Research Seminar session followed by a one-hour small-group discussion with faculty discussion leaders. Student will learn to understand current developments in statistical genetic/genomic methods and current analytic issues in genetic epidemiology; become familiar with sources of methodology literature for the design and analysis of investigations in statistical genetics, statistical genomics, and genetic epidemiology; develop critical evaluation skills for underlying theory and/or applications of current study designs and statistical analysis methods; and develop skills in communication and presentation in an interdisciplinary setting.

This course will introduce students to the statistical methods suitable for analysing large observational data, data constructed from multiple institutional databases, web­based data, and any data that may benefit from non­classical approaches. The theory will be presented as an extension of classical tools such as linear and logistic regression, parametric hypothesis testing, multivariate Gaussian theory, to make it more intuitive and accessible.

Data science is a recently emerged field increasingly used in various areas and applications in industry, academia, and government. Health Data Science refers to the application of Data Science methods and principles to large real-world complex data and problems in health. Some examples include health administrative data, electronic health records, clinical registries, while these can be also linked with Patient Recorded Outcomes, Genomic Data, Lab data among others. This course will provide an introduction to data science and how it can be useful for applications in population health and public health outcomes. The focus will be on Data Science analytics methods, such as applied machine and statistical learning, using the R statistical software system. Some theoretical background will be presented but the focus will be on hands-on practical application using large health data.

This course will introduce the foundational concepts in predictive modeling with emphasis on blending interpretability and generalizability which are important in health sciences applications. The classical linear models, such as ordinary least squares and logistic regressions will be recast as supervised learning tools for regression and classification tasks, respectively, and then extended through common techniques of basis expansion and regularization. A second part of the course will focus on many aspects of estimating prediction performance of any method. The course will blend theoretical framework and simulation-based computational approaches to enhance the understanding of issues such as bias-variance trade-off, overfitting, generalization performance to prepare students to critically appraise the use of, and effectively deploy simple and more complex supervised learning techniques. The course will also serve as a preparation for studying more complex methods (such as in CHL5229H, for which this course is meant to be a prerequisite).

This course provides a comprehensive introduction to spatial and spatiotemporal methods used in public health studies. It is designed as two related parts: 1) spatial and spatiotemporal statistics and 2) geostatistical regression models.

In Part 1, students will gain a solid understanding of spatial and spatiotemporal data structure, statistical methods. and techniques utilized in disease mapping and pattern analyses.

Part 2 introduces geostatistic data structure and regression models used for disease modelling and health risk prediction.

This course includes lectures and tutorials, using case studies to illustrate concepts, theory, and methodologies. R programming will also be provided to enhance practical knowledge and real-word data applications.

This course is intended to provide students with an opportunity to learn, or have some exposure to, a variety of current advanced statistical methods used in health sciences research. Seminars will be given by the course director, other faculty members, and guest speakers. In addition, informal presentations and group discussions will also be arranged.

The course is intended to improve presentation and writing skills for scientific careers, to give exposure to a wide range of Biostatistics research questions, to provide professional development opportunities, and to engage students and faculty in the biostatical community. Students are required to register and enroll for at least three consecutive years during their PhD program. The class will meet on a weekly basis for a one-hour seminar, given by either PhD student(s), guest speaker(s), or faculty member(s). Students are required to present their research work, especially work in progress, at least once a year depending on the stage of their PhD studies.

This is an intense introductory graduate-level course in public health policy. This course will introduce students to the structure of the Canadian health system and the scope of and types of literatures that make up public health policy discourse. Students will learn concepts of underlying oppression in public health policies and critical anti-racist approaches to public health policy. Students will also develop critical reading skills in this field and understand the socio-political dimensions of public health policy and the major theoretical frameworks used to analyze policy change.

This course will provide skills and training in public health policy analysis and evaluation, including economic analysis. Students will be exposed to approaches to public health policy analyses and evaluations and will become familiar with the nuts and bolts of conducting analysis and evaluation. Readings and discussions will offer critical perspectives on the practice of policy analysis and evaluation through public health case studies highlighting challenges, limitations and strengths.

This seminar course is intended to promote the development of essential professional skills to support ongoing learning and workforce preparation for first year MPH epidemiology students. All of the work for this seminar course will take place within the classroom through a short introductory lecture followed by experiential learning activities and written reflection. The goal of the course is to provide opportunities for the development and practice of core competencies related to leadership, communication (written and verbal), knowledge translation, critical reading, and information synthesis. We will also cover issues related to ethics and data use and governance.

Each week we will work on a different professional skill. The class will start with an introduction to the topic providing relevant background and context. This will be followed by a group activity. Each week we will read and discuss a scientific paper in class together. Class will end with a reflection on that day's activities.