Online Lectures

Transparency and Reproducibility in Pharmacoepidemiology: Moving Towards a Better Approach

About the presentation: Transparency and reproducibility are increasingly recognized as essential to credible real-world evidence, but putting these principles into practice can feel burdensome. In this talk, we make the case for why a better approach is needed and show how practical tools such as preregistered protocols, HARPER, code sharing, and transparency statements can be integrated into everyday research. Drawing on real-world examples, we will explore how transparent practices help us distinguish credible evidence, understand conflicting findings, and build reusable research infrastructure that can ultimately make rigorous RWE generation more efficient, not less.

About the presenters:

Anton Pottegård is a clinical epidemiologist whose work focuses on strengthening methodological rigor and building shared infrastructure for reproducible research. He has contributed to preregistration initiatives, development of pragmatic common data models to enable code reuse and validation across sites, and embeds open science into routine research workflows and professional norms by promoting structured transparency statements and teaching courses focused on transparency and reproducibility.

Shirley V. Wang is a leader in advancing transparency and reproducibility in healthcare database research. Her work has been informed by large-scale empirical assessments of RWE study reproducibility, resulting in development of structured protocol templates, graphical frameworks for study design, and preregistration infrastructure through the Real-World Evidence Registry. She translates open science principles into applied research practice by mentoring investigators to treat transparency as a baseline expectation for credible science.

From Data Source Description to Bias Quantification: Applications of the DIVERSE Framework

About the presentation: In this webinar, the speakers will present findings from the DIVERSE Initiative and their relevance to CNODES research. First, Dr. Pajouheshnia will introduce the DIVERSE framework and tool for describing real-world data sources, including nine key dimensions to describe data sources to support study replicability. Following this, he will present and compare two examples of data sources that have been mapped to the DIVERSE tool, 1) the UK CPRD Aurum and 2) CNODES Ontario and will share reflections on what we can learn from these mappings and how they support replicability of research. Next, Dr. Gini will explain how information collected in the DIVERSE tool can be used to understand and quantify measurement error in studies. Dr Gini. will briefly introduce DAiReCt (Disease, Action, Record, Content), a novel framework designed to capture the causal structure hidden behind phenotypes of variables and will introduce the component strategy that leverages real world data diversity in studies to quantify the share of true positives missed by phenotypes.

About the presenters:

Dr. Romin Pajouheshnia is an epidemiologist at RTI Health Solutions and is based in the Netherlands. He has more than a decade of experience in pharmacoepidemiology, clinical epidemiology, and epidemiologic methods research. His work focuses on generating real-world evidence using secondary data sources, with interests spanning drug safety, comparative effectiveness research, and methodological research. Together with Rosa Gini, he co-leads the International Society for Pharmacoepidemiology Databases (ISPE) Special Interest Group’s “DIVERSE Initiative”, which aims to improve the transparency and replicability of real-world evidence through the improved description and understanding of secondary data sources.

Dr. Rosa Gini is Head of the Pharmacoepidemiology Unit at ARS Toscana (Regional Health Agency of Tuscany, Italy). She is a mathematician by training, and her path as a data scientist led her to specialize in the secondary use of healthcare data for pharmacoepidemiologic and health services research. Her work focuses on developing methods, standards, and tools to support transparent, reliable, and timely generation of real-world evidence for health policy and regulatory decision-making. She is a former Chair of the ISPE Databases Special Interest Group, chairs the European Network of Centres for Pharmacoepidemiology & Pharmacovigilance’s Working Group on Transparency and Scientific Independence, and serves as Vice President of the Vaccine Monitoring Collaboration for Europe (VAC4EU).

Applying Novel Study Designs to Study Treatment Discontinuation: Contrasting Cloning, Censoring, and Weighting to a Prevalent New User Approach

About the presentation: Estimating the effects of discontinuation, deprescribing, or otherwise stopping drug treatments using observational data has been and continues to be extremely challenging. Two newer study designs (cloning, censoring, and weighting and a prevalent new user approach) have the potential to avoid immortal time and other self-inflicted biases common in studies of discontinuation. That said, what exactly each design estimates differ in subtle but important ways. This presentation will go over these distinctions and discuss important strengths and weaknesses of these designs for drug discontinuation studies.

About the presenter:  Michael Webster-Clark is a pharmacoepidemiologist who specializes in epidemiologic methods research, with a focus on novel study designs and extending inferences to external target populations.

Unlocking Innovation in Population-Based Health Research with Synthetic Data

About the presentation: Synthetic health data are generated from existing electronic health datasets, so that they maintain the properties of the original data. Producing and sharing synthetic data may shorten the time to conduct studies and reduce legislative and ethical barriers to data sharing. Our goal is to provide an overview of current uses of synthetic health data in the Canadian context, and to discuss potential innovative uses of synthetic electronic health data for CNODES.

About the presenters:

Lisa Lix is a Distinguished Professor in the College of Community and Global Health at the University of Manitoba and Director of Data Science in the George & Fay Yee Centre for Healthcare Innovation. She holds a Tier 1 Canada Research Chair in Methods for Electronic Health Data Quality.

Hassan Maleki Golandouz is a PhD candidate in the College of Community and Global Health at the University of Manitoba. His research focuses on developing fairness-aware and equitable predictive models using electronic health data, as well as investigating the role of synthetic data in their development and validation. He also has a background in Computer Engineering and Artificial Intelligence.