ArticleJournal of the National Cancer Institute2010
Designing a randomized clinical trial to evaluate personalized medicine: a new approach based on risk prediction.
Article in Journal of the National Cancer Institute, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 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
6 citing papers in PubMed.
- Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects.BMJ (Clinical research ed.) · 2018Review
- Innovative methods for the identification of predictive biomarker signatures in oncology: Application to bevacizumab.Contemporary clinical trials communications · 2017Article
- Exploiting high-throughput cell line drug screening studies to identify candidate therapeutic agents in head and neck cancer.BMC pharmacology & toxicology · 2014Article
- Simplifying electronic data capture in clinical trials: workflow embedded image and biosignal file integration and analysis via web services.Journal of digital imaging · 2014Article
- Nipple discharge of CA15-3, CA125, CEA and TSGF as a new biomarker panel for breast cancer.International journal of molecular sciences · 2014Article
- Incorporation of prognostic and predictive factors into glioma clinical trials.Current oncology reports · 2013Review
Corrections and comments
- Retraction · 2011-01-12Doing the Right Thing · Error in Analyses · · see also : https://academic.oup.com/jnci/article/103/5/445/905180
- Commented on by
Authors and funding
2 authors.
Funding
Abstract
We define personalized medicine as the administration of treatment to only persons thought most likely to benefit, typically those at high risk for mortality or another detrimental outcome. To evaluate personalized medicine, we propose a new design for a randomized trial that makes efficient use of high-throughput data (such as gene expression microarrays) and clinical data (such as tumor stage) collected at baseline from all participants. Under this design for a randomized trial involving experimental and control arms with a survival outcome, investigators first estimate the risk of mortality in the control arm based on the high-throughput and clinical data. Then investigators use data from both randomization arms to estimate both the effect of treatment among all participants and among participants in the highest prespecified category of risk. This design requires only an 18.1% increase in sample size compared with a standard randomized trial. A trial based on this design that has a 90% power to detect a realistic increase in survival from 70% to 80% among all participants, would also have a 90% power to detect an increase in survival from 50% to 73% in the highest quintile of risk.
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.