Observational studyBMC medical research methodology2023
Evaluating and improving real-world evidence with Targeted Learning.
Observational study in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Considerations for the Integration of Clinical Trials and Real-World Data.Clinical pharmacology and therapeutics · 2026Review
- AI-Enabled Real-World Evidence in Oncology: A Statistical Perspective for Regulatory Decisions.Therapeutic innovation & regulatory science · 2026Review
- Seasonal differences in depressive affect, worry-related sleep disturbance, and low energy among UK young people.PloS one · 2026Article
- Evaluating and improving real-world evidence with Targeted Learning.BMC medical research methodology · 2023Observational
- A causal roadmap for generating high-quality real-world evidence.Journal of clinical and translational science · 2023Review
- An application of the Causal Roadmap in two safety monitoring case studies: Causal inference and outcome prediction using electronic health record data.Journal of clinical and translational science · 2023Article
- A Visual Analysis of Patient-Reported Outcomes in Lung Cancer From 2013 to 2023.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundThe Targeted Learning roadmap provides a systematic guide for generating and evaluating real-world evidence (RWE). From a regulatory perspective, RWE arises from diverse sources such as randomized controlled trials that make use of real-world data, observational studies, and other study designs. This paper illustrates a principled approach to assessing the validity and interpretability of RWE.
methodsWe applied the roadmap to a published observational study of the dose-response association between ritodrine hydrochloride and pulmonary edema among women pregnant with twins in Japan. The goal was to identify barriers to causal effect estimation beyond unmeasured confounding reported by the study's authors, and to explore potential options for overcoming the barriers that robustify results.
resultsFollowing the roadmap raised issues that led us to formulate alternative causal questions that produced more reliable, interpretable RWE. The process revealed a lack of information in the available data to identify a causal dose-response curve. However, under explicit assumptions the effect of treatment with any amount of ritodrine versus none, albeit a less ambitious parameter, can be estimated from data.
conclusionsBefore RWE can be used in support of clinical and regulatory decision-making, its quality and reliability must be systematically evaluated. The TL roadmap prescribes how to carry out a thorough, transparent, and realistic assessment of RWE. We recommend this approach be a routine part of any decision-making process.
Indexed as
Identifiers
What 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.