Economic Evaluations of Next-Generation Precision Oncology: A Critical Review
Bibliographic record
Abstract
PURPOSE: Precision oncology has the potential to improve patient health and reduce treatment costs. Yet the up-front cost of genomic testing with next-generation sequencing (NGS) technologies can be prohibitive. Our study is a structured review of economic evaluations of precision oncology informed by NGS. The aim is to characterize the availability and scope of economic evidence. MATERIALS AND METHODS: We searched Medline (PubMed), Embase (Ovid), and Web of Science databases for English-language full-text peer-reviewed articles published between 2000 and 2016. We focused our search on articles that estimated the benefit of precision oncology in relation to its costs. We excluded studies that did not undertake full economic evaluations or did not focus on NGS technologies. We reviewed all included studies and summarized key methodological and empirical study characteristics. RESULTS: Fifty-five economic evaluations met our inclusion criteria. The number of published studies increased steadily, from three studies between 2005 and 2007 to 26 between 2014 and 2016. Most studies evaluated multiplex panels (86%). We found testing was frequently used to predict prognosis (67%), to diagnose patients (24%), or to identify targeted therapeutic options (7%). Methods and cost effectiveness differed according to NGS technology, test strategy, and cancer type. Deterministic and probabilistic analyses were typically used to characterize parameter and decision uncertainty (91% and 75%). CONCLUSION: Although the availability of economic evidence examining precision oncology increased over time, methods used often did not align with current guidelines. Future evaluations should undertake extensive sensitivity analysis to address all sources of uncertainty associated with rapidly changing NGS technologies. Furthermore, additional research is needed evaluating the cost effectiveness of more comprehensive next-generation technologies before implementing these on a wider scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".