Evidence map›Paper›PMID 41105104›Full record

ArticleStatistics in medicine2025

Determining the Optimal Sequence of Multiple Tests.

Lucas Böttcher, Stefan Felder

Abstract read
In one paragraph

Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lucas BöttcherDepartment of Computational Science and Philosophy, Frankfurt School of Finance and Management, Frankfurt am Main, Germany.
Stefan FelderFaculty of Business and Economics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-5029-7274

Funding

Army Research Office W911NF-23-1-0129hessian.AI
6 · The paper itself

Abstract

The use of multiple tests can improve medical decision making by balancing the benefits of correctly treating ill patients and avoiding unnecessary treatment for healthy individuals against the potential harms of missed diagnoses, inappropriate treatments, and the costs and risks associated with testing. We quantify the incremental net benefit (INB) of single and multiple tests by accounting for a patient's pre-test probability of disease and the associated benefits, harms, and cost of treatment and testing. We decompose the INB into two components: one that captures the value of information provided by the test, independent of the cost and possible harm of testing, and another that accounts for test costs and harm. Next, we examine conjunctive, disjunctive, and majority aggregation functions, demonstrating their application through examples in prostate cancer, colorectal cancer, and stable coronary artery disease diagnostics. Our approach complements traditional threshold and decision-curve analysis by varying both the pre-test probability of disease and the cost-benefit trade-off of treatment to identify the region over which a given test provides the highest INB. Using empirical test and cost data, we compute decision boundaries to determine when conjunctive, disjunctive, majority, or even single tests are optimal, and, for combinations of tests, in what order they should be administered. In all three application examples, we find that the optimal choice and sequence of tests jointly depend on the probability of disease and the cost-benefit trade-off of treatment. An online tool that visualizes the INB for combined tests is available at https://optimal-testing.streamlit.app/.

Indexed as

Diagnostic Tests, RoutineColorectal NeoplasmsCoronary Artery DiseaseCost-Benefit AnalysisHumansMaleModels, StatisticalProstatic Neoplasmscombination testingdiagnostic testsoptimal testingreceiver operating characteristicstest thresholdtreatment thresholdvalue of information

Identifiers

PMID41105104
PMCPMC12533563

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.