The CAN-AIM team develops novel methods, using prospective longitudinal cohorts, to study drug safety and effectiveness.

The CAnadian Network for Advanced Interdisciplinary Methods for comparative effectiveness research’ (CAN-AIM) team was selected by Canadian Agency of Drugs and Technologies in Health (CADTH) as a collaborator for their Post-Market Drug Evaluation Network (PMDE). CAN-AIM was previously funded by the Drug Safety and Effectiveness Network (DSEN) through a partnership between CIHR and Health Canada (2011-2022). Our mandate is to enhance the validity and accuracy of research on drugs’ comparative effectiveness and safety.

Comparative effectiveness research compares existing therapies to understand which treatment works best for which patients and, additionally, which treatments pose the greatest harm. To do so, CAN-AIM responds to queries from regulatory bodies on the drug safety and efficacy of medications on conditions such as hypertension, diabetes, rheumatoid arthritis, rheumatic diseases, inflammatory bowel disease and cancer. CAN-AIM also responds to queries focusing on drug use patterns, pharmacoeconomic analyses, patient preferences, and prescription patterns.

Since 2011, CAN-AIM has undertaken successful collaborations between our researchers (providing clinical and methodological expertise and efficient access to data sources) and the policymakers (from Health Canada and other agencies), who use the knowledge we generate. Our approach uses both clinical and population-based cohorts and administrative data to produce timely answers to queries. This combined approach’s critical advantage is that while administrative databases provide longitudinal information on drug exposure and morbidities, it typically lacks data on essential confounders, such as life habits and clinical variables. However, many prospective cohorts available to our team often provide detailed clinical and laboratory data on confounders typically not measured in the administrative databases. Our innovative methodologies use these two data sets to explore and understand the resulting associations and risks more fully.