This course will present all the basic definitions, and overarching concepts and principles in perinatal pharmacoepidemiology. This will be given as a formal lecture with time for questions at the end.

This course will explore how to engage patients and public partners and knowledge users in research, going through the main steps and elements to consider for co-creation of research with patients and other partners.

This course will outline importance and methodological considerations surrounding the integration of sex and gender in pharmacoepidemiology through lectures, group discussions and examples drawn from the medical literature.

Understanding the concepts behind equity, diversity and inclusion (EDI) is fundamental to achieving EDI goals and taking a more proactive approach to ensuring that different demographics are better represented in society at large. Addressing the dynamics of EDI is becoming a must and requires a shift in the way we work and deliver health care. Developing an EDI plan that incorporates a vision, mission, concrete actions and evaluation measures is key. Adopting and implementing a scientific and reasoned approach to EDI becomes essential for students, faculty, and health care providers to prevent discriminatory bias against people with different backgrounds and characteristics. By being more aware of our beliefs, committing to change our environment, and taking action, we will help individuals and organizations be more inclusive for those who work there and those who receive health care.

This course is the first part of a two-course series designed to introduce the basic principles of drug discovery and development. This first part will cover an overview of a pharmacologic product from drug discovery to full development, followed by a focus on target identification, drug design and synthesis, and efficacy determination.

This course is the second part of a two-course series designed to introduce the basic principles of drug discovery and development. This second part will briefly review an overview of a pharmacologic product from drug discovery to full development, followed by a focus on required toxicology studies and clinical trials.

This course will cover the foundations of neuropharmacology and provide an overview of the brain at the cellular and molecular levels to understand why receptors and transporters account for more than 50% of all therapeutic targets and what the future directions are.

This course will focus on reverse pharmacology and the use of state-of-the-art molecular tools, approaches that, over the past 10 to 15 years, have allowed to elucidate the role and function of any given protein and improve our understanding of the organization of brain circuits in complex behaviors.

This journal club aims to deepen critical appraisal skills and develop critical thinking for analyzing and reading scientific articles as it pertains to the study of adverse effects of environmental perturbations on behavior in animal models (in vivo). This session will provide an interactive and social opportunity for peer-to-peer learning, with time for questions and group discussion.

Trainees in the 2026-2027 cohort will be divided into 2 groups, and each group will present a review of one of the following articles during the journal club.

✓  Mourlon V et al. (2010). Maternal deprivation induces depressive-like behaviours only in female rats.
✓  Baudin A et al. (2012). Maternal deprivation induces deficits in temporal memory and cognitive flexibility and exaggerates synaptic plasticity in the rat medial prefrontal cortex.

This course will cover basic pharmacogenomic terminology, research methods and key limitations of these studies.

Pre-readings (PDFs available in the Member Area):
✓  Blumenfeld YJ et al. (2010). Maternal-fetal and neonatal pharmacogenomics: a review of current literature.
✓  Crisafulli C et al. (2019). Pharmacogenetic and pharmacogenomic discovery strategies.

This course will summarize the value and limitations of Big and Small data drug outcome studies and why both study types improve the rigour of each other.

Pre-readings (PDFs available in the Member Area):
✓  Bissel M (2013). Reproducibility: The risks of the replication drive.
✓  Allison DB & Fineberg HV (2020). EPA’s proposed transparency rule: Factors to consider, many; planets to live on, one.

This course will explore key methods of implementation science in both perinatal epidemiology and pharmacogenomic studies and will have participants designing implementation science methods for a perinatal pharmacogenomic study.

Pre-reading (PDF available in the Member Area):
✓  Phillips CA et al. (2022). Implementation science in pediatric oncology: A narrative review and future directions.

This course will describe key thresholds for evidence-based pharmacogenetic testing as well as limitations and value of commercial panels.

This journal club will evaluate the quality of a perinatal outcome study and appraise the value of a fetal pharmacogenomic study. The use of both study types in succession will be discussed.

More specifically, this journal club aims to deepen critical appraisal skills and develop critical thinking for analyzing and reading scientific articles related to the concepts covered in the Pharmacogenomics Module. This session will provide an interactive and social opportunity for peer-to-peer learning, with time for questions and group discussion.

Trainees in the 2026-2027 cohort will be divided into 2 groups, and each group will present a review of one of the following articles during the journal club.

✓  Moriello C et al. (2021). Off-label postpartum use of domperidone in Canada: a multidatabase cohort study.
✓  Raymond M et al. (2022). Fetal pharmacogenomics: A promising addition to complex neonatal care.

This course will introduce trainees to basic pharmacoepidemiology principles and concepts, including study designs and their basic features. The course will also provide an understanding of bias and confounding in pharmacoepidemiology.

This intermediate-level course will build upon the first course in this module and present methods used to correct for confounding, including propensity score matching, instrumental variables, time-varying exposures in pregnancy, etc. This will be given as a formal lecture with question periods built in and some breakout group work.

Pre-readings (PDFs available in the Member Area):
✓  Snowden JM et al. (2018). The Curse of the Perinatal Epidemiologist: Inferring Causation Amidst Selection.
✓  Ukah UV et al. (2022). Time-related biases in perinatal pharmacoepidemiology: A systematic review of observational studies.
✓  Stürmer T et al. (2006). A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods.
✓  Vigod SN et al. (2015). Antipsychotic drug use in pregnancy: high dimensional, propensity matched, population based cohort study.

This course follows the first part of the intermediate-level pharmacoepidemiology course and will introduce quasi-experimental methods that can be used to better target causal research questions. This will be given as a formal lecture with question periods built in and some breakout group work.

Pre-readings (PDFs available in the Member Area):
✓  Choudhry NK et al. (2010). At Pitney Bowes, value-based insurance design cut copayments and increased drug adherence.
✓  Schummers L et al. (2022). Abortion Safety and Use with Normally Prescribed Mifepristone in Canada.
✓  Smith LM et al. (2017). Strategies for evaluating the assumptions of the regression discontinuity design: a case study using a human papillomavirus vaccination programme.
✓  Socha PM et al. (2024). Antenatal corticosteroids and newborn respiratory outcomes in twins: A regression discontinuity study.

This course will explore common data sources used in pharmacoepidemiology and methodological considerations through didactic lectures, group discussions, and examples from the medical literature.

Pre-reading (PDF available in the Member Area):
✓  Dormuth CR et al. (2021). Comparison of Pregnancy Outcomes in Patients Treated With Ondansetron vs. Alternative Antiemetic Medications in a Multinational, Population-Based Cohort.

This journal club aims to deepen critical appraisal skills and develop critical thinking for analyzing and reading scientific articles related to the concepts covered in the Pharmacoepidemiology Module. This session will provide an interactive and social opportunity for peer-to-peer learning, with time for questions and group discussion.

Trainees in the 2026-2027 cohort will be divided into 2 groups, and each group will present a review of one of the following articles during the journal club.

✓  Cleary B et al. (2019). Methadone, Pierre Robin sequence, and other congenital anomalies: case–control study.
‍✓  Andersen SL et al. (2019). Maternal Thyroid Function, Use of Antithyroid Drugs in Early Pregnancy, and Birth Defects.

This course will target statisticians, epidemiologists, data scientists and other quantitative researchers or students with a basic familiarity with regression modelling.

The course will cover general strategies for fitting prediction models for continuous, categorical and time-to-event outcomes, including: exploratory analysis and data visualization; missing data imputation; covariate selection; model specification; model validation and calibration; handling non-linearity; and choosing between conventional statistical models and machine learning models, including the differences between these types of models.

Extensive use of R, RStudio and Frank Harrell's Hmisc and rms R packages will be used in the course material and case studies/examples. The course will follow the general philosophy of Regression Modeling Strategies (2nd Edition textbook) by Frank Harrell. Reading this textbook is optional but recommended.

This course will target statisticians, epidemiologists, data scientists and other quantitative researchers or students with a basic familiarity with regression modelling.

The course will cover general strategies for fitting prediction models for continuous, categorical and time-to-event outcomes, including: exploratory analysis and data visualization; missing data imputation; covariate selection; model specification; model validation and calibration; handling non-linearity; and choosing between conventional statistical models and machine learning models, including the differences between these types of models.

Extensive use of R, RStudio and Frank Harrell's Hmisc and rms R packages will be used in the course material and case studies/examples. The course will follow the general philosophy of Regression Modeling Strategies (2nd Edition textbook) by Frank Harrell. Reading this textbook is optional but recommended.

This relatively advanced course in applied biostatistics is designed for graduate students and researchers in statistics or biostatistics, epidemiology or pharmacoepidemiology, data science, as well as public health, who have a good understanding of multivariable regression and some knowledge of applied survival analysis, especially the Cox model.

The first part will focus on the non-technical conceptual introduction to relevant statistical modeling methods and real-life applications (mostly in pharmacoepidemiology), with an overview of different modelling approaches that may be considered to analyze associations between a time-varying drug exposure and time to a clinical endpoint (e.g., an adverse event or death). Then, the importance of considering potential (i) latency between exposure and change in risk and/or (ii) cumulative effects of past exposures will be discussed. Next, the Weighted Cumulative Exposure (WCE) methodology will be explained in a way accessible to participants without formal background in statistics or biostatistics.

The second part will focus on practical issues related to the use of the R package WCE to analyze real-world pharmacoepidemiology data. The way data have to be prepared for WCE analyses and the steps necessary to carry out these analyses will be explained.

This course will discuss the fundamentals for applying propensity score methods in observational research with a focus on pharmacoepidemiology. We will cover the basic principles behind causal inference concepts and motivate their use for reducing the impact of confounding due to observed covariates. The emphasis of the course will be on the practical application of these methods using examples in the R programming language and will focus specifically on matching and inverse probability of treatment weighting. Strategies to address common complications in propensity score analyses will be discussed.

This course will explore how to engage with knowledge users in research by covering different types of knowledge synthesis methods for decision-making. The main steps and elements to consider for co-creation of research with knowledge users will be discussed.

This course will provide an introduction to the use of machine learning in applied research by presenting the assumptions and concepts underlying the application of machine learning to conduct research with health and behavioral data.

This course will introduce artificial intelligence and machine learning in perinatal research through a concept-driven, demonstration-based approach. The course will emphasize core principles, common pitfalls, and practical decision-making in healthcare applications.

Using interactive examples based on perinatal scenarios (e.g., predicting preterm birth using a synthetic dataset), participants will explore how machine learning models behave under realistic conditions such as small datasets, class imbalance, missing data, and population differences.

This course will provide a comprehensive journey through the historical development of artificial intelligence, showcasing its pivotal role in enabling advancements in perinatal research. Attendees will gain insights into applied examples, including the use of health administrative data for maternal-fetal healthcare and computer vision applications for outcome prediction using ultrasound imaging. The session will conclude with a visionary framework for the future of artificial intelligence in medicine and practical guidance for conceptualizing future artificial intelligence research studies.