ReviewBMJ open2025
Transporting trial results to synthetic real-world populations in order to estimate real-world effectiveness of newly marketed medicines.
Review in BMJ open, 2025. 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionReal-world effectiveness of a new treatment is relevant information for patients, healthcare professionals and payers, especially when patients encountered in routine clinical care differ significantly from those recruited in the randomised controlled trials (RCTs) that led to approval. However, obtaining effect estimates can be challenging when a new drug has only recently been marketed and real-world data (RWD) are not yet available. For new breast cancer (BC) therapies, we illustrate how RCT inferences can be transported to a target population and how a synthetic population can be generated to mimic a target population for which no RWD is yet available. METHODS AND ANALYSIS: In our framework, we defined the data-generating process for the RCT population and the real-world (target) population with confounders, effect-modulating covariates and survival times as outcomes. First, we conducted generalisability and transportability (G&T) analyses to transport the RCT results to the simulated target population, applying the inverse probability of sampling weighting and outcome model-based estimator approach. We then used Synthea to generate a synthetic target population based on German BC survival rates and combined both approaches into a coherent strategy.
resultsEffect estimates (HRs with 95% CIs) transported from the RCT to our defined target population closely matched the expected real-world effect (RCT: 0.68 (0.65; 0.71); real-world: 0.75 (0.71; 0.79); transported from RCT: 0.76 (0.71; 0.81)). BC survival rates were very similar between observed and synthetic data (prediction error in absolute survival rates: 1.62%).
conclusionCombining G&T with synthetic data may inform decision-making in situations where RWD are not (yet) available.
Indexed as
Identifiers
What Socratic 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 Socratic graph.