Evidence map›Paper›PMID 37466257›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2023

Stability of clinical prediction models developed using statistical or machine learning methods.

Richard D Riley, Gary S Collins

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers, 15 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
129citing papers in PubMed, 15 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

129 citing papers in PubMed, 15 syntheses or guidelines pooled it.

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  16. Development and validation of an anastomotic risk score for use in a randomized clinical trial on defunctioning stoma use in low anterior resection for rectal cancer.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2025
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69 more citing papers are in PubMed but not listed here.

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.

Richard D RileyInstitute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.ORCID 0000-0001-8699-0735
Gary S CollinsCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.ORCID 0000-0002-2772-2316

Funding

Cancer Research UK 27294Cancer Research UK C49297/A27294Medical Research Council MR/V038168/1
6 · The paper itself

Abstract

Clinical prediction models estimate an individual's risk of a particular health outcome. A developed model is a consequence of the development dataset and model-building strategy, including the sample size, number of predictors, and analysis method (e.g., regression or machine learning). We raise the concern that many models are developed using small datasets that lead to instability in the model and its predictions (estimated risks). We define four levels of model stability in estimated risks moving from the overall mean to the individual level. Through simulation and case studies of statistical and machine learning approaches, we show instability in a model's estimated risks is often considerable, and ultimately manifests itself as miscalibration of predictions in new data. Therefore, we recommend researchers always examine instability at the model development stage and propose instability plots and measures to do so. This entails repeating the model-building steps (those used to develop the original prediction model) in each of multiple (e.g., 1000) bootstrap samples, to produce multiple bootstrap models, and deriving (i) a prediction instability plot of bootstrap model versus original model predictions; (ii) the mean absolute prediction error (mean absolute difference between individuals' original and bootstrap model predictions), and (iii) calibration, classification, and decision curve instability plots of bootstrap models applied in the original sample. A case study illustrates how these instability assessments help reassure (or not) whether model predictions are likely to be reliable (or not), while informing a model's critical appraisal (risk of bias rating), fairness, and further validation requirements.

Indexed as

Machine LearningModels, StatisticalComputer SimulationHumansPrognosiscalibrationfairnessprediction modelstabilityuncertainty

Identifiers

PMID37466257
PMCPMC10952221

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LicenceCC BY
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Registered trials

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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.