ReviewCancer2012
Data for cancer comparative effectiveness research: past, present, and future potential.
Review in Cancer, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- A Systematic Review of Electronic Medical Record Driven Quality Measurement and Feedback Systems.International journal of environmental research and public health · 2022Pooled it
- Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system.Translational lung cancer research · 2021Article
- Monitoring public health reporting: data tracking in cancer registries.Online journal of public health informatics · 2018Article
- A validation of clinical data captured from a novel Cancer Care Quality Program directly integrated with administrative claims data.Pragmatic and observational research · 2017Article
- Comparative Effectiveness of Oxaliplatin Versus 5-flourouricil in Older Adults: An Instrumental Variable Analysis.Epidemiology (Cambridge, Mass.) · 2015Article
- Quality-of-life assessment after palliative interventions to manage malignant ureteral obstruction.Cardiovascular and interventional radiology · 2013Article
- Validation of a registry-derived risk algorithm based on treatment protocol as a proxy for disease risk in childhood acute lymphoblastic leukemia.BMC medical research methodology · 2013Article
- A framework for understanding cancer comparative effectiveness research data needs.Journal of clinical epidemiology · 2012Article
- Big data for population-based cancer research: the integrated cancer information and surveillance system.North Carolina medical journalArticle
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 at 3 institutions in 1 country.
Funding
Abstract
Comparative effectiveness research (CER) can efficiently and rapidly generate new scientific evidence and address knowledge gaps, reduce clinical uncertainty, and guide health care choices. Much of the potential in CER is driven by the application of novel methods to analyze existing data. Despite its potential, several challenges must be identified and overcome so that CER may be improved, accelerated, and expeditiously implemented into the broad spectrum of cancer care and clinical practice. To identify and characterize the challenges to cancer CER, the authors reviewed the literature and conducted semistructured interviews with 41 cancer CER researchers at the Agency for Healthcare Research and Quality's Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) Cancer CER Consortium. Several data sets for cancer CER were identified and differentiated into an ontology of 8 categories and were characterized in terms of strengths, weaknesses, and utility. Several themes emerged during the development of this ontology and discussions with CER researchers. Dominant among them was accelerating cancer CER and promoting the acceptance of findings, which will necessitate transcending disciplinary silos to incorporate diverse perspectives and expertise. Multidisciplinary collaboration is required, including those with expertise in nonexperimental data, statistics, outcomes research, clinical trials, epidemiology, generalist and specialty medicine, survivorship, informatics, data, and methods, among others. Recommendations highlight the systematic, collaborative identification of critical measures; application of more rigorous study design and sampling methods; policy-level resolution of issues in data ownership, governance, access, and cost; and development and application of consistent standards for data security, privacy, and confidentiality.
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.