ReviewPharmacoEconomics2026
Bayesian Dynamic Borrowing to Enhance Evidence for New Therapies.
Review in PharmacoEconomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estimating and interpreting treatment effects (TE) for rare or delayed clinical outcomes is often challenging. To address this, researchers may incorporate additional evidence sources, including historical trial data and concurrent information from intermediate outcomes. In this article, we present Bayesian dynamic borrowing (BDB) as a principled framework for integrating such data while maintaining control of bias and Type I error. Using hypothetical trials of a novel high-efficacy therapy for multiple sclerosis, we provide a step-by-step demonstration of how BDB can be used to combine an imprecise TE estimate for a final outcome with a prediction derived from historical data and information on a concurrent intermediate outcome. Our illustration includes calibration of BDB to meet desired Type I error and power properties, and sensitivity analyses to assess robustness to assumption violations. We also discuss key considerations for applying BDB in regulatory decision making and health technology assessment contexts.
Identifiers
42698058What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.