The CAMCCO-L platform aims to provide training in English and French that complements the disciplinary academic training offered in universities by creating a virtual training environment that supports the development of skills essential to translational and transectorial research as well as interdisciplinary collaboration in perinatal research on medications.
CAMCCO-L is a new transdisciplinary virtual learning model offered at no cost that will help develop leaders in perinatal research on medications who will be then able to meet the complex interdisciplinary challenges of the current environment of this field of research.
As part of the CAMCCO-L training, selected trainees will also have the opportunity to participate in a Summer School on Drug Development and complete a research internship.
None, this track is open to all who wish to further their interdisciplinary knowledge and expertise in perinatal research on medications.
None, this track allows you to benefit from the CAMCCO-L training without commitment or obligation.
The internship and Summer School are optional steps in the 1-year curriculum.
or
and
Formal academic supervision by a CAMCCO-L mentor throughout the track
Mandatory enrollment in the Overarching Principles Module and at least 2 other modules complementary to the university training field
• Webinars
• Journal Clubs
Online courses offered on CAMCCO-L will be given live weekly (synchronous) via the CAMCCO-L Member Area.
After each course, the presentations and recordings of the majority of courses will be accessible in the archives of the CAMCCO-L Member Area to those registered for the courses.
Depending on the teaching language, a translation by subtitles in English or French will be offered for the recordings of all archived courses.
It will be possible to take the courses in any specific order, however, courses in some modules may be prerequisites for subsequent courses in the module.
To access the archived material of a course, you must have registered for the course in your Member Area.
Presenter: Anick Bérardlink
Teaching Language: English
The first course of the CAMCCO-L Overarching Principles Module 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.
Define all basic terminology used in perinatal pharmacoepidemiology, i.e., gestational age, prematurity, low birth weight, gestational diabetes and hypertension, pre-eclampsia and eclampsia, Apgar score, malformations, etc.
Definition and importance of organogenesis
Medication exposure time-windows of interest for important adverse pregnancy outcomes
Presenters: Amanda Kirby and Andrea Triccolink
Teaching Language: English
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 a patient partner involved in perinatal research.
How to identify and engage with patients and public partners in research
Appreciation and conflicts of interest policies for patients and public partners in research
Capacity-building/training for patients and public partners in research
Evaluating and reporting patient and public engagement in research
How to consider ethics and equity, diversity, and inclusion in research
How to embed equity, diversity, and inclusion in the research process
Lessons learned and resources available on patient and public engagement in research
Presenters: Anaïs Lacasselink and Louise Pilotelink
Teaching Language: French
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.
Understand the importance of integrating sex and gender in pharmacoepidemiology
Overview the different options for measuring sex and gender in existing databases studies or in the context of prospective data collection
Know best practices in terms of sex- and gender-based statistical analysis
Presenters: Dimitri Girierlink and Tania Sabalink
Teaching Language: English
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.
Gain a better understanding of the concepts of equity, diversity, and inclusion and the various laws that frame it
Identify our implicit biases and how they can affect the way we provide health services
Identify the risks, blind spots and benefits of equity, diversity and inclusion
Reflect on action plans to integrate equity, diversity and inclusion into our practices
Presenter: Louise Winnlink
Teaching Language: English
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.
At the end of this course, trainees will be able to:
Understand the critical role of basic science research in drug discovery
Articulate the principles of pre-clinical pharmacology studies
Describe how the components of ADME studies are assessed
Summarize how the principles of drug discovery are used to select appropriate lead candidates
Presenter: Louise Winnlink
Teaching Language: English
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.
At the end of this course, trainees will be able to:
Describe the principles of preclinical studies and how they support clinical trials
Articulate the types of toxicology studies needed with respect to drug development
Compare and contrast the study design of different types of clinical trials
Discuss the social and economic pressures involved in drug discovery and development using orphan drugs as examples
Presenter: Bruno Giroslink
Teaching Language: English
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.
At the end of this course, trainees will be able to understand/define:
Brain cells and organization
Neurotransmitters
Anatomy
Metabotropic and ionotropic and receptors and their characterization
Plasmic transporters
Vesicular transporters
Presenter: Bruno Giroslink
Teaching Language: English
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.
At the end of this course, trainees will be able to understand:
Transgenesis
Homologous recombination
Optogenetics
Chemogenetics
CRISPR/Cas 9
Presenter: Bruno Giroslink
Présentateur : Bruno Giros
Teaching Language: English
lilili No archived video recording
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 from the CAMCCO-L cohort will be divided into 2 groups, and each group will be assigned an article to review. Each group will present their review during the journal club.
Articles to read before the course
✓ Mourlon M 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
Develop critical appraisal skills for analyzing and reading scientific articles on the study of adverse effects of environmental pertubations on behavior
Identify and analyze the main methodological strengths and weaknesses of scientific articles
Develop collaborative and teamwork skills with respect to discussions surrounding the scientific articles
Demonstrate enhanced presentation skills with respect to summarizing scientific articles
Presenter: Bruce Carletonlink
Teaching Language: English
lilili No archived video recording
This journal club is an opportunity for trainees to explore the opportunity to include genomic data in pharmacoepidemiology study analyses. Genomic data are increasingly available for patient cohorts. This journal club will address how important is genomic information in understanding drug response and the mechanistic basis of outcomes found in a pharmacoepidemiological study.
Trainees from the CAMCCO-L cohort will be divided into 2 groups, and each group will be assigned an article to review. Each group will present their review during the journal club. This session will be interactive, with time for questions and discussion.
Articles to read before the course
✓ Moriello C et al. Off-label postpartum use of domperidone in Canada: a multidatabase cohort study. DOI
✓ Crisafulli C et al. Pharmacogenetic and pharmacogenomic discovery strategies. DOI
Describe pharmacogenetic and pharmacogenomic discovery strategies
Examine a pharmacoepidemiological study that included a pharmacogenomic variable in the analysis
Summarize the application of pharmacogenomics in maternal-fetal and neonatal populations
Presenter: Bruce Carletonlink
Teaching Language: English
This course will have participants evaluating the quality of a perinatal outcome study and appraising phenotyping and value of perinatal outcome and pharmacogenomic studies. This will be given as a 2-hour formal lecture followed by a panel discussion for the last hour covering the integration of study designs.
Article to read before the course
✓ Blumenfeld YJ et al. Maternal-fetal and neonatal pharmacogenomics: a review of current literature. DOI
Evaluate the quality of a perinatal outcome study
Appraise the phenotyping of a perinatal pharmacogenomic study
Interpret the value of both types of studies examining the same outcome
Presenter: Bruce Carletonlink
Teaching Language: English
This course will summarize the value and limitations of Big and Little Data drug outcome studies and why both study types improve the rigour of each other. This will be given as a 2-hour formal lecture followed by a panel discussion for the last hour covering the triangulation of Big and Little Data.
Summarize the value and limitations of Big Data drug outcome studies
Summarize the value and limitations of Little Data drug outcome studies
Appraise the value of both study designs examining the same outcome
Presenter: Bruce Carletonlink
Teaching Language: English
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. This will be given as a 2-hour formal lecture followed by a panel discussion for the last hour covering the implementation science in genetic perinatal pharmacoepidemiology.
Describe key methods of implementation science in perinatal epidemiology studies
Describe key methods of implementation science in pharmacogenomic studies
Design implementation science methods for a perinatal pharmacogenomic study
Presenter: Bruce Carletonlink
Teaching Language: English
This course will describe key thresholds for evidence-based pharmacogenetic testing as well as limitations and value of commercial panels. This will be given as a 2-hour formal lecture with a part focusing on clinical examples of evidence-based pharmacogenetic testing followed by a 1-hour panel discussion covering commercial testing panels.
List three key thresholds for the use of evidence for clinical pharmacogenetic testing
Describe key limitations of commercial pharmacogenetic testing panels
Determine the value of pharmacogenetic testing from clinical examples
Presenter: Sherif Eltonsylink
Teaching Language: English
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.
Introduce the basic principles, concepts, and study designs in pharmacoepidemiology
Provide an overview of the basic features of cohort and case-control designs
Provide an introduction of bias and confounding in pharmacoepidemiology
Explore how bias and confounding are introduced, and how they can be avoided or controlled
Presenters: Gillian Hanleylink and Azar Mehrabadilink
Teaching Language: English
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.
Review common sources of bias in pharmacoepidemiologic studies during pregnancy, immortal time bias, and selection bias (e.g. left-truncation bias)
Introduce methods for addressing these sources of bias, including, but not limited to propensity score matching, instrumental variables, time-varying exposures, etc.
Discuss study designs that reduce the risk of these sources of bias
Presenters: Gillian Hanleylink and Azar Mehrabadilink
Teaching Language: English
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.
Introduce the role of quasi-experimental designs in pharmacoepidemiology
Cover some novel uses of quasi-experimental designs to address important perinatal epidemiology research questions
Outline some useful quasi-experimental designs for pharmacoepidemiology research in pregnancy, including, but not limited to regression discontinuity design, interrupted time series, etc.
Presenters: Brandace Winquistlink and Anick Bérardlink
Teaching Language: English
This course will explore common data sources used in pharmacoepidemiology and methodological considerations through didactic lectures, group discussions, and examples from the medical literature.
Introduce the concept of real-world data in the context of perinatal pharmacoepidemiology
Provide an overview of common data sources and pregnancy cohorts
Review coding classification systems and data definitions
Explore harmonization of data models across jurisdictions and quality considerations in data linkage
Validation studies done using the Quebec Pregnancy Cohort
Presenter: Anick Bérardlink
Teaching Language: English
lilili No archived video recording
This last course of the Pharmacoepidemiology Module will use three published manuscripts to review and summarize all concepts seen within the module. This session will be interactive with questions and answers.
People registered for the journal club will be divided into 2 to 3 groups and each group will be assigned an article to review. Each group will present the critical review of their article during the journal club.
Articles to read before the course
✓ Muanda FT et al. Use of antibiotics during pregnancy and risk of spontaneous abortion. DOI
✓ 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
Develop critical appraisal skills for analyzing and reading scientific articles
Identify and analyze the main methodological strengths and weaknesses of scientific articles
Develop collaborative and teamwork skills with respect to discussions surrounding scientific articles
Demonstrate enhanced presentation skills with respect to summarizing scientific articles
Presenter: Steven Hawkenlink
Teaching Language: English
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).
Methods for exploring, describing and understanding your data in preparation for regression modeling
Fitting multivariable regression models appropriate for continuous, categorical, and time to event outcomes
Address issues of sample size and overfitting
Approaches to addressing missing data
Handling complex non–linear or non–additive relationships
Testing/quantifying associations between one or more predictors and the response, and interpreting the fitted model
Model validation and calibration to evaluate predictive accuracy and identify overfitting
Learn the differences between machine learning and statistical models, and how to choose the best approach for a given problem
Presenter: Steven Hawkenlink
Teaching Language: English
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).
Methods for exploring, describing and understanding your data in preparation for regression modeling
Fitting multivariable regression models appropriate for continuous, categorical, and time to event outcomes
Address issues of sample size and overfitting
Approaches to addressing missing data
Handling complex non–linear or non–additive relationships
Testing/quantifying associations between one or more predictors and the response, and interpreting the fitted model
Model validation and calibration to evaluate predictive accuracy and identify overfitting
Learn the differences between machine learning and statistical models, and how to choose the best approach for a given problem
Presenters: Areti Angeliki Veroniki and Andrea Triccolink
Teaching Language: English
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.
At the end of this course, trainees will be able to:
Describe co-creation and why it is important in research
Identify knowledge users who can be engaged in research
Identify different types of knowledge synthesis for decision-making (systematic review, meta-analysis, network meta-analysis, scoping reviews, overview of reviews, rapid reviews)
Describe how to select a knowledge synthesis method for a particular research question
Presenters: Areti Angeliki Veroniki and Andrea Triccolink
Teaching Language: English
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.
At the end of this course, trainees will be able to:
Demonstrate the basic principles of pairwise meta-analysis
Identify effect measures used in meta-analysis for dichotomous and continuous outcomes
Introduce heterogeneity, and common meta-analytical approaches (common and random effects models)
Describe important aspects of interpreting meta-analysis results using real-life examples
Understand the usefulness of NMA in medical research
Communicate how direct and indirect evidence can be combined within NMA and how it is related to pairwise meta-analysis
Understand principles and prerequisite assumptions in NMA, and investigate heterogeneity, intransitivity, and inconsistency
Understand and present different methods for ranking interventions
Become familiar with ways of presenting the results of network meta-analysis
Presenter: Christopher Gravellink
Teaching Language: English
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.
At the end of this course, trainees will be able to:
Understand the principles underlying inferring causal effects in observational data
Understand the concept of directed acyclic graphs (DAGs) and confounding
Obtain a conceptual understanding of propensity score analysis and the circumstances under which they may be used
Using R based examples, learn how to implement propensity score matching and inverse probability of treatment weighting
Understand how to compute and diagnostics and interpret the findings of propensity score analyses
Presenter: Marc Lanovazlink
Teaching Language: English
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.
At the end of this course, trainees will be able to:
Define machine learning and basic related concepts
Describe, in words the general functioning of at least one machine learning algorithm
Explain the logic underlying the research methodology used in machine learning
Identify research questions that can be investigated using machine learning
Presenter: Anahita Doosti
Teaching Language: English
lkink No archived course notes
In an era where artificial intelligence and machine learning are shaping advancements in many fields, extracting meaningful information from massive datasets has become crucial, especially in health research. This course is tailored for researchers and students in the perinatal and medications field, aiming to give them a robust grounding in machine learning's essential aspects.
Comprendre l'interaction entre l'intelligence artificielle, l'apprentissage automatique et la science des données, en particulier en recherche périnatale sur les médicaments et pour la recherche de la Canadian Mother-Child Cohort (CAMCCO)
Assess how artificial intelligence can enhance the depth and efficiency of your studies
Formulate health research questions suitable for machine learning analysis
Recognize potential pitfalls or hurdles while integrating artificial intelligence into your research
Grasp the workings of machine learning and your pivotal role in its application to CAMCCO studies
Presenter:Anahita Doosti
Teaching Language: English
lkink No archived course notes
Delving deeper, this course equips researchers with specialized tools and strategies vital for data analysis using machine learning.
Recognize the intricacies of data, with an emphasis on large datasets
Understand the three categories of machine learning and their applications in perinatal studies on medications and Canadian Mother Child Cohort (CAMCCO) studies
Choose the right machine learning approach for specific research questions
Comprehend the constraints in machine learning and their impact on health research
Navigate the Machine Learning Process Lifecycle for designing machine learning solutions tailored for CAMCCO and perinatal research on medications problems
Organize and preprocess data for effective machine learning application
Assemble a cross-disciplinary team, ensuring a holistic approach to research
Presenter :Anahita Doosti
Teaching Language: English
lkink No archived course notes
Ensuring the ethical application of artificial intelligence in health research is paramount. This workshop is designed to foster a strong sense of ethical artificial intelligence practices among researchers and students in the perinatal and medications field.
Realize the significance of governance in artificial intelligence for ethical health research
Understand the advantages of embedding ethics at the design stage of artificial intelligence research tools
Familiarize with best practices and frameworks to ensure artificial intelligence's ethical application in perinatal studies on medications and Canadian Mother-Child Cohort (CAMCCO) studies
Pinpoint and mitigate risks to foster greater acceptance of your research in the academic community and beyond
Stay updated with the latest standards and regulations concerning artificial intelligence tools in health research
Presenter: Serge McGraw
Teaching Language: English
lilili No archived video recording
This workshop will introduce trainees (MSc, PhD and postdocs) on how to organize and write fellowship applications. Trainees will learn what are the common mistakes observed during the reviewing process and how we can avoid them. This workshop will concentrate on Canadian Institutes of Health Research (CIHR) awards, but the ideas will apply to the majority of fellowships.
Dr. Serge McGraw is an Associate Professor in the Department of Obstetrics and Gynecology at the University of Montreal. His principal research interests are focused on the harmful developmental outcomes caused by epigenetic instabilities arising from alterations in DNA methylation profiles during early embryogenesis. By combining in vitro stem cell models as well as in vivo mouse models with multi-omics sequencing approaches, his laboratory aims at understanding how perturbations in the early embryonic program may lead to epigenetics errors driving in the occurrence of prenatal or after birth developmental disorders.
Presenter: Hailey Banack
Teaching Language: English
lilili No archived video recording
The focus of the webinar will be on methods for conducting quantitative bias analysis in epidemiologic research, touching on both deterministic and probabilistic bias analysis for confounding, selection bias, and misclassification.
Dr. Banack is an Assistant Professor in the Epidemiology Division at the Dalla Lana School of Public Health at the University of Toronto. Dr. Banack’s research is focused on obesity, body composition, aging, and chronic disease, with a particular interest in novel methodologies to address complex statistical issues in aging research.
Presenter : Sonia Grandi
Teaching Language: English
lilili No archived video recording
The safety profile of many medications and vaccines frequently used in pregnancy is unknown due to the exclusion or underrepresentation of pregnant and lactating individuals in post-marketing surveillance studies. The target trial approach using observational data has been proposed as an alternative solution to generate much-needed information on drug effectiveness and safety for pregnant and lactating individuals. This webinar will discuss the design of target trials using observational data in perinatal pharmacoepidemiology and highlight key considerations and challenges in implementing this approach to study medication use in pregnancy.
Dr. Grandi is a Scientist in the Child Health Evaluative Sciences Program at the Research Institute of the Hospital for Sick Children and an Assistant Professor in the Division of Epidemiology in the Dalla Lana School of Public Health at the University of Toronto. Her research focuses on the influence of preconception and perinatal exposures on the short- and long-term health of mothers and children, with a specific interest in cardiometabolic health. She is also interested in the application of novel methods in perinatal epidemiology and leveraging administrative health data to help inform clinical practice.
Presenter: Anick Bérard
Teaching Language: English
lilili No archived video recording
The Canadian Mother-Child Initiative on Drug Safety in Pregnancy (CAMCCO) is an interdisciplinary initiative, which includes research, training, and knowledge transfer. This webinar will introduce the Canadian Hub on Medications and Pregnancy and show how to access this essential infrastructure.
With the collaboration and funding from Health Canada, CIHR, and CFI, the Pan-Canadian Mother-Child Cohort (CAMCCO-Researchlink) Surveillance Program was developed in April 2019 and the data platform has been successfully utilized to generate real-world evidence on medication use during pregnancy, antipsychotic use and COVID-19 in pregnancy.
The Training Platform (CAMCCO-Learn) provides over 78 hours of bilingual virtual courses, and in-person meetings targeting graduate students, early career researchers, and patient partners (CIHR and Strategy for Patient-Oriented Research (SPOR)).
The Knowledge-Transfer Platform (CAMCCO-Outreach) is leveraging research and training to build a strong national Hub to provide valid and reliable information on medications and pregnancy for the Canadian general population and the prescribers.
Presenter: Isabelle Boucoiran
Teaching Language: English
lilili No archived video recording
REDCap (Research Electronic Data Capture) is a web-based data capture tool used to create, manage, and deploy research databases and surveys. It has built-in functionalities for data importing and exporting, quality checking, reporting, and basic statistics summarization. This workshop will provide an overview of REDCap and demonstrations of core features.
Presenter: Yi Li
Teaching Language: English
lilili No archived video recording
The webinar will discuss the newly extended methods in causal mediation analysis that accommodate effect modifiers. An empirical drug example will be provided after the introduction of methods. The corresponding R package “regmedint” that implements the new methods will also be introduced.
Ms. Yi Li is a PhD candidate in epidemiology at McGill University. Her research interests are causal inference methods, machine learning, and applications in pharmacoepidemiology. Before enrolling in McGill's PhD program, she was previously trained in Departments of Biostatistics at University of North Carolina – Chapel Hill and Harvard T.H. Chan School of Public Health.
The next Summer School will take place from June 29 to July 5, 2024 at the Ribeirão Preto Medical School of the University of São Paulo in Brazil.
People wishing to apply for an internship within the Mitacs Accelerate Program can also apply for a Mitacs bursary.