ArticleFrontiers in oncology2017
Big Data in Designing Clinical Trials: Opportunities and Challenges.
Article in Frontiers in oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.RMD open · 2019Pooled it
- Using Billing Codes to Create a Pediatric Functional Status e-Score for Children Receiving Inpatient Rehabilitation.Archives of physical medicine and rehabilitation · 2023Article
- Predicting risk factors for pediatric mortality in clinical trial research: A retrospective, cross-sectional study using a Healthcare Cost and Utilization Project database.Journal of clinical and translational science · 2023Article
- The evolution of Big Data in neuroscience and neurology.Journal of big data · 2023Article
- A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research.International journal of molecular sciences · 2022Review
- Shaken not stirred: Big data meets randomized controlled trial.Medical journal, Armed Forces India · 2021Article
- Bridging the Brain and Data Sciences.Big data · 2021Article
- Applying advanced technologies to improve clinical trials: a systematic mapping study.Scientometrics · 2021Article
- The application of big data to cardiovascular disease: paths to precision medicine.The Journal of clinical investigation · 2020Review
- Role of Big Data in Cardiovascular Research.Journal of the American Heart Association · 2019Review
- Big data in IBD: a look into the future.Nature reviews. Gastroenterology & hepatology · 2019Review
- Exploiting the Circadian Clock for Improved Cancer Therapy: Perspective From a Cell Biologist.Frontiers in genetics · 2019Article
- Aging Well: Using Precision to Drive Down Costs and Increase Health Quality.Advances in geriatric medicine and research · 2019Article
- Treatment data and technical process challenges for practical big data efforts in radiation oncology.Medical physics · 2018Article
- Head and Neck Radiation Therapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: Results From the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium.Advances in radiation oncologyArticle
- Combination of a Big Data Analytics Resource System With an Artificial Intelligence Algorithm to Identify Clinically Actionable Radiation Dose Thresholds for Dysphagia in Head and Neck Patients.Advances in radiation oncologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emergence of big data analytics resource systems (BDARSs) as a part of routine practice in Radiation Oncology is on the horizon. Gradually, individual researchers, vendors, and professional societies are leading initiatives to create and demonstrate use of automated systems. What are the implications for design of clinical trials, as these systems emerge? Gold standard, randomized controlled trials (RCTs) have high internal validity for the patients and settings fitting constraints of the trial, but also have limitations including: reproducibility, generalizability to routine practice, infrequent external validation, selection bias, characterization of confounding factors, ethics, and use for rare events. BDARS present opportunities to augment and extend RCTs. Preliminary modeling using single- and muti-institutional BDARS may lead to better design and less cost. Standardizations in data elements, clinical processes, and nomenclatures used to decrease variability and increase veracity needed for automation and multi-institutional data pooling in BDARS also support ability to add clinical validation phases to clinical trial design and increase participation. However, volume and variety in BDARS present other technical, policy, and conceptual challenges including applicable statistical concepts, cloud-based technologies. In this summary, we will examine both the opportunities and the challenges for use of big data in design of clinical trials.
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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.