ArticleMolecular oncology2012
Paying for personalized care: cancer biomarkers and comparative effectiveness.
Article in Molecular oncology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Mass spectrometry-based proteomics of FFPE tissues: progress, limitations, and clinical translation barriers.Clinical proteomics · 2025Review
- Molecular and genetic markers in hepatocellular carcinoma:World journal of gastrointestinal pathophysiology · 2022Article
- Molecular biomarkers and precision medicine in colorectal cancer: a systematic review of health economic analyses.Oncotarget · 2019Review
- Value of a Hypothetical Pharmacogenomic Test for the Diagnosis of Statin-Induced Myopathy in Patients at High Cardiovascular Risk.Molecular diagnosis & therapy · 2018Article
- Biomarkers: Delivering on the expectation of molecularly driven, quantitative health.Experimental biology and medicine (Maywood, N.J.) · 2018Review
- Rethinking Causation in Cancer with Evolutionary Developmental Biology.Biological theory · 2018Article
- Cost Implications of Value-Based Pricing for Companion Diagnostic Tests in Precision Medicine.PharmacoEconomics · 2016Article
- Precision Medicine and Non-Colorectal Cancer Liver Metastases: Fiction or Reality?Viszeralmedizin · 2015Review
- Preliminary Development and Evaluation of an Algorithm to Identify Breast Cancer Chemotherapy Toxicities Using Electronic Medical Records and Administrative Data.Journal of oncology practice · 2015Article
- Issues surrounding the health economic evaluation of genomic technologies.Pharmacogenomics · 2013Review
- Personalizing health care: feasibility and future implications.BMC medicine · 2013Review
- Paying for personalized care: cancer biomarkers and comparative effectiveness.Molecular oncology · 2012Article
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
Genomic-based diagnostics can play a key role in creating a more efficient healthcare system by directing patients toward beneficial therapies and away from therapies that pose substantial risk or are unlikely to improve outcomes for the patient. We outline how the value provided by diagnostics is closely linked to a range of factors including magnitude of health outcome improvement, avoiding adverse effect, diagnostic parameters, process of care, resource utilization, and costs. Comparative effectiveness approaches to evidence generation, including health outcome measurements, quality of life, economic analyses, decision modeling, and pragmatic clinical trials, can be used to provide stakeholders with a range of information to inform treatment, guidelines, coverage, and reimbursement decisions. Evidence of comparative effectiveness can also help support value-based reimbursement of cancer biomarkers and treatment strategies as means of paying for personalized medicine.
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.