Can We Move Beyond a Best Guess? The Feasibility of Transportability Analysis to Estimate the Effect of GLP-1 Inhibitors in the Canadian Population
Author(s)
Rebecca Metcalfe, BA, MA, PhD1, Quang Vuong, MSc2, Jay J. Park, PhD3;
1Centre for Advancing Health Outcomes, Vancouver, BC, Canada, 2Core Clinical Sciences, Vancouver, BC, Canada, 3McMaster University, Department of Health Research Methods, Evidence, and Impact, Hamilton, ON, Canada
1Centre for Advancing Health Outcomes, Vancouver, BC, Canada, 2Core Clinical Sciences, Vancouver, BC, Canada, 3McMaster University, Department of Health Research Methods, Evidence, and Impact, Hamilton, ON, Canada
OBJECTIVES: It is estimated that over 3 million Canadians have type 2 diabetes. Recently approved GLP-1 inhibitors offer promise for improved control of blood sugar and weight management for adults with type 2 diabetes. However, several landmark trials did not include any Canadian sites. As a result, the extent to which these trial findings apply to the Canadian population is unclear. The aim of this work was to assess the feasibility of using transportability analysis to estimate the population-level impact of GLP-1 inhibitors in Canada.
METHODS: We performed a data landscaping assessment to identify potential data sources to enable transportation of GLP-1 inhibitor treatment effects to the Canadian population. Our search targeted data repositories available to researchers in Canada. We aimed to find source study datasets from phase 3 clinical trials of GLP-1 inhibitors in type 2 diabetes, and target datasets that were representative of the Canadian population living with type 2 diabetes. Once potential data sources were identified, we mapped the available variables in the source and target datasets to determine feasibility of transportability analysis.
RESULTS: Our data landscaping assessment identified one GLP-1 inhibitor source dataset that was available to Canadian researchers: the SURPASS-3 trial comparing tirzepatide to insulin degludec for control of HbA1C and weight management. Our assessment also identified several potential target datasets that were representative of the Canadian population. Variable mapping found that the 2017-2018 Statistics Canada Canadian Community Health Survey (CCHS) contained sufficient variables to create alignment between the source and target populations.
CONCLUSIONS: Our findings show that transporting GLP-1 inhibitor treatment effects to the Canadian population is feasible. Of particular note given the given substantial regional variation in type 2 diabetes rates across Canada, the granularity of the CCHS data will allow us to quantify the variation in potential benefit across provinces and territories.
METHODS: We performed a data landscaping assessment to identify potential data sources to enable transportation of GLP-1 inhibitor treatment effects to the Canadian population. Our search targeted data repositories available to researchers in Canada. We aimed to find source study datasets from phase 3 clinical trials of GLP-1 inhibitors in type 2 diabetes, and target datasets that were representative of the Canadian population living with type 2 diabetes. Once potential data sources were identified, we mapped the available variables in the source and target datasets to determine feasibility of transportability analysis.
RESULTS: Our data landscaping assessment identified one GLP-1 inhibitor source dataset that was available to Canadian researchers: the SURPASS-3 trial comparing tirzepatide to insulin degludec for control of HbA1C and weight management. Our assessment also identified several potential target datasets that were representative of the Canadian population. Variable mapping found that the 2017-2018 Statistics Canada Canadian Community Health Survey (CCHS) contained sufficient variables to create alignment between the source and target populations.
CONCLUSIONS: Our findings show that transporting GLP-1 inhibitor treatment effects to the Canadian population is feasible. Of particular note given the given substantial regional variation in type 2 diabetes rates across Canada, the granularity of the CCHS data will allow us to quantify the variation in potential benefit across provinces and territories.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
CO156
Topic
Clinical Outcomes
Topic Subcategory
Comparative Effectiveness or Efficacy
Disease
SDC: Diabetes/Endocrine/Metabolic Disorders (including obesity)