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 be presented in collaboration with two patient partners involved in perinatal research.

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 Part 1 of a series of two that will introduce trainees to the basic principles of drug discovery and development. In this Part 1, an overview of a pharmacologic product from drug discovery to full development will be covered followed by a focus on target identification, drug design and synthesis, and efficacy determination.

This course is Part 2 of a series of two that will introduce trainees to the basic principles of drug discovery and development. In this Part 2, a very brief overview of a pharmacologic product from drug discovery to full development will be reviewed followed by a focus on required toxicology studies, clinical trials and orphan drugs.

More details coming soon!

In this course, basis for neuropharmacology will be covered and we will have an overview of the cellular and molecular brain, to understand why receptors and transporters represent more than 50% of all therapeutical targets and what are the future directions.

This webinar will introduce (1) a Japanese database available for pharmacoepidemiologic research, and (2) researches using the dataset. Audiences will learn what is the Japanese database via actual research results obtained using the dataset for future collaboration researches.

Since 10-15 years, reverse pharmacology and the use of state of the art molecular tools allowed to decipher the role and function of any given protein and to deconstruct brain circuitry organization in complex behavior.

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 our 2024-2025 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. Maternal deprivation induces depressive-like behaviours only in female rats. DOI ↗
✓  Baudin A et al. Maternal deprivation induces deficits in temporal memory and cognitive flexibility and exaggerates synaptic plasticity in the rat medial prefrontal cortex. DOI ↗

More details coming soon!

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

Pre-readings:
✓  Blumenfeld YJ et al. Maternal-fetal and neonatal pharmacogenomics: a review of current literature. DOI ↗
✓  Crisafulli C et al. Pharmacogenetic and pharmacogenomic discovery strategies. DOI ↗

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

Pre-readings:
✓  Bissel M. Reproducibility: The risks of the replication drive. DOI ↗
✓  Allison DB & Fineberg HV. EPA's proposed transparency rule: Factors to consider, many; planets to live on, one. DOI ↗

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:
✓  Phillips CA et al. Implementation science in pediatric oncology: A narrative review and future directions. DOI ↗

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

In this webinar, we will review historical events and bioethical issues that have led to the systematic exclusion of pregnant and lactating people in clinical trials and discuss challenges to studying these populations, and strategies to overcome them.

This course 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.

Trainees in our 2024-2025 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. Off-label postpartum use of domperidone in Canada: a multidatabase cohort study. DOI ↗
✓  Raymond M et al. Fetal pharmacogenomics: A promising addition to complex neonatal care. DOI ↗

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 webinar will introduce spontaneous reporting (SR) databases and discuss how they fit into pharmacovigilance processes within the drug product lifecycle. Attendees will learn how to mine the FDA’s Adverse Event Reporting System (FAERS) database for drug safety hypothesis generation, or signal detection, using disproportionality analysis algorithms in R. The challenges in the interpretation of detected signals will be discussed, and the forms of bias specific to these analyses will be reviewed. Finally, some of the challenges in the practical use of SR data for drug safety surveillance will be overviewed, and potential ways to mitigate these concerns will be highlighted.

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

This intermediate phamacoepidemiology course will build upon the introductory course 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.

This follow-up course to Part 1 of intermediate pharmacoepidemiology 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.

This course will use published manuscripts to review and summarize all concepts seen within the Pharmacoepidemiology Module. This session will be interactive with questions and answers.

Trainees in our 2024-2025 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. Methadone, Pierre Robin sequence and other congenital anomalies: case-control study. DOI ↗
‍✓  Andersen SL et al. Maternal Thyroid Function, Use of Antithyroid Drugs in Early Pregnancy, and Birth Defects. DOI ↗

This course will target statisticians, epidemiologists, data scientists and other quantitative researchers/students with a basic familiarity with regression modeling. The course will cover general strategies for fitting prediction models for continuous, categorical and timeto-event outcomes, including: exploratory analysis/data visualization; missing data imputation; covariate selection; model specification; model validation/calibration; handling non-linearity; and choosing between conventional statistical models and machine learning models (and 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 casestudies/examples. The course will follow the general philosophy of Regression Modelling Strategies - 2nd Edition textbook by Frank Harrell (Optional, but recommended course textbook; all necessary readings/lecture notes will be provided for participants).

This course will target statisticians, epidemiologists, data scientists and other quantitative researchers/students with a basic familiarity with regression modeling. The course will cover general strategies for fitting prediction models for continuous, categorical and timeto-event outcomes, including: exploratory analysis/data visualization; missing data imputation; covariate selection; model specification; model validation/calibration; handling non-linearity; and choosing between conventional statistical models and machine learning models (and 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 casestudies/examples. The course will follow the general philosophy of Regression Modelling Strategies - 2nd Edition textbook by Frank Harrell (Optional, but recommended course textbook; all necessary readings/lecture notes will be provided for participants).

This, relatively advanced, course in applied biostatistics is designed for graduate trainees and researchers in (bio-)statistics, (pharmaco-)epidemiology, data science, as well as public health, who have good understanding of multivariable regression and some knowledge of applied survival analysis (especially of the Cox model).

The 1st 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 modeling 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 for participants without formal background in statistics or biostatistics.

The 2nd 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.

Part 1 of 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.

Part 2 of this course will explore important topics in evidence synthesis, building on their previous introductory training on knowledge synthesis. Attendees will be introduced to the basic principles and concepts of network meta-analysis (NMA). The key assumptions of NMA, including transitivity and consistency between different sources of evidence in a network will be exemplified.

This course involves an introduction to the use of machine learning in applied research. Specifically, the instructor will review the assumptions and concepts underlying the application of machine learning to conduct research with health and behavioral data.

More information to come by November 2024!

More information to come by November 2024!