Back

Real-world cardiovascular effects of liraglutide: transportability analysis of the LEADER trial

Josey, K. P.; Liu, W.; Warsavage, T.; Medici, M.; Kvist, K.; Derington, C. G.; Reusch, J. E. B.; Ghosh, D.; Raghavan, S.

2025-05-13 endocrinology
10.1101/2025.05.12.25327466 medRxiv
Show abstract

Appropriate use of recently approved type 2 diabetes treatments depends on external validity of landmark clinical trials (RCTs) in real-world populations that may differ from trial participants. This study transported effect estimates from the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial, a placebo-controlled RCT of liraglutide on cardiovascular outcomes, onto nested real-world cohorts within the Veterans Affairs (VA) healthcare system. Risk differences (RD) in survival outcomes, approximated using pseudo-observations of individual survival probabilities, were estimated with augmented inverse probability weighting after balancing baseline characteristics between RCT and target samples using approximate balancing weights. Transported effects of liraglutide compared to placebo on major adverse cardiovascular events (MACE) and all-cause mortality in veterans ("VA-weighted LEADER") consistently overlapped the treatment effects observed in LEADER: MACE RD at 3 years of 2.0% [95% CI 0.8, 3.2] in VA-weighted LEADER versus 1.6% [0.3, 2.9] in LEADER; all-cause mortality RD at 3 years of 1.5% [0.6, 2.4] in VA-weighted LEADER versus 0.9% [-0.09, 1.9] in LEADER. The benefits of liraglutide observed in LEADER generalized to veterans with diabetes -- real-world evidence that can guide diabetes treatment decisions and formulary policies for a high-risk population underrepresented in RCTs.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.