ArticleStatistics and its interface2011
Bayesian decision analysis for choosing between diagnostic/prognostic prediction procedures.
Article in Statistics and its interface, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 12 citations in OpenAlex.
- Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis.Journal of medical Internet research · 2025Article
- Enhancing Dementia Classification for Diverse Demographic Groups: Using Vision Transformer-Based Continuous Scoring of Clock Drawing Tests.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025Article
- From pre-test and post-test probabilities to medical decision making.BMC medical informatics and decision making · 2024Article
- The usefulness of SAGE score in predicting high pulse wave velocity in hypertensive patients: a retrospective cohort study.Frontiers in cardiovascular medicine · 2024Article
- On determining the most appropriate test cut-off value: the case of tests with continuous results.Biochemia medica · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
New diagnostic procedures and prognostic markers are continually being developed for a wide range of medical complaints. Medical institutions are therefore regularly faced with the decision as to whether to replace an existing procedure with a new one. The decision to adopt a new method is primarily based on diagnostic/predictive accuracy and cost-effectiveness, but this trade-off is not usually considered in a formal decision-theoretic way. The decision process for diagnostic procedures is complicated by the fact that diagnostic decisions are typically based on thresholding one or more continuous variables. Therefore, a formal decision process should account for uncertainty in the optimal threshold value for each diagnostic procedure. We here propose a Bayesian decision approach based on maximizing expected utility (incorporating accuracy and costs) with respect to diagnostic procedure and threshold level simultaneously. The Bayesian decision approach is illustrated via an application comparing the utility of different bone mineral density (BMD) measurements for determining the need for preventative treatment of osteoporotic hip fracture in elderly patients.
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.