ArticleClinical pharmacology and therapeutics2020
Reweighting Randomized Controlled Trial Evidence to Better Reflect Real Life - A Case Study of the Innovative Medicines Initiative.
Article in Clinical pharmacology and therapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Framework for the synthesis of non-randomised studies and randomised controlled trials: a guidance on conducting a systematic review and meta-analysis for healthcare decision making.BMJ evidence-based medicine · 2022Pooled it
- Mapping the Use of Real-World Evidence Across the EU Health Technology Assessment Regulation: Methodological Considerations, Challenges, and Opportunities for Harmonization.Journal of market access & health policy · 2026Article
- Model-based standardization using multiple imputation.BMC medical research methodology · 2024Article
- Transporting Comparative Effectiveness Evidence Between Countries: Considerations for Health Technology Assessments.PharmacoEconomics · 2024Article
- Generalizability of clinical trial efficacy results to a real-world population: An example in migraine prevention.Journal of managed care & specialty pharmacy · 2023Article
- Testing the "RCT augmentation" methodology: A trial simulation study to guide the broadening of trials eligibility criteria and inform on effectiveness.Contemporary clinical trials communications · 2023Article
- Health technology assessment for the acute and preventive treatment of migraine: A position statement of the International Headache Society.Cephalalgia : an international journal of headache · 2021Article
- The Promise, and Challenges, of Methods to Enhance the External Validity of Randomized Trial Results.Clinical pharmacology and therapeutics · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evidence from randomized controlled trials available for timely health technology assessments of new pharmacological treatments and regulatory decision making may not be generalizable to local patient populations, often resulting in decisions being made under uncertainty. In recent years, several reweighting approaches have been explored to address this important question of generalizability to a target population. We present a case study of the Innovative Medicines Initiative to illustrate the inverse propensity score reweighting methodology, which may allow us to estimate the expected treatment benefit if a clinical trial had been run in a broader real-world target population. We learned that identifying treatment effect modifiers, understanding and managing differences between patient characteristic data sets, and balancing the closeness of trial and target patient populations with effective sample size are key to successfully using this methodology and potentially mitigating some of this uncertainty around local decision making.
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.