Randomized controlled trials are considered the gold standard for evaluating treatment efficacy, but real-world effectiveness may vary due to stricter inclusion criteria in clinical trials compared to the target treated population. Policymakers, payers, and clinicians often question how well results from clinical trials apply to real-world scenarios.
In a study by Lugo-Palacios et al. (2024), the researchers aimed to determine the most effective second-line treatment for type 2 diabetes in the real world. They assessed the use of dipeptidyl peptidase‐4 inhibitors (DPP4i) and sulfonylureas (SU) as add on therapies to metformin for patients with type 2 diabetes in England, focusing on glycemic control as the primary endpoint.
The study analyzed subpopulations within the target population, dividing them into ‘RCT eligible’ and ‘RCT ineligible’ groups based on eligibility criteria from published RCTs. The authors compared average treatment effects (ATEs) for ‘RCT eligible’ to RCT benchmark data and examined conditional average treatment effects (CATEs) for the overall target population, considering factors like age, ethnicity, baseline HbA1c, and BMI.
Using local instrumental variables (LIV) methodology, the authors found that ATEs for ‘RCT-eligible’ population aligned with published RCT results, but CATEs varied in magnitude between included and excluded subpopulations. The study highlights the importance of considering real-world data in treatment effectiveness assessments for different patient groups.
The study’s approach and findings are crucial for understanding how treatments perform outside of controlled trials, offering insights into the nuances of real-world effectiveness for diverse patient populations.
