Evidence map›Paper›PMID 26996421›Full record

ReviewEndocrinology and metabolism (Seoul, Korea)2016

How to Establish Clinical Prediction Models.

Yong Ho Lee, Heejung Bang, Dae Jung Kim

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Endocrinology and metabolism (Seoul, Korea), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
95citing papers in PubMed, 11 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

95 citing papers in PubMed, 11 syntheses or guidelines pooled it.

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  4. Exploring the role of health-related quality of life measures in predictive modelling for oncology: a systematic review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
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  5. Systematic review of 99 extremity bone malignancy survival prediction models.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2025
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35 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

3 authors.

Yong Ho LeeDepartment of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea. yholee@yuhs.ac.ORCID http://orcid.org/0000-0002-6219-4942
Heejung BangDivision of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA.
Dae Jung KimDepartment of Endocrinology and Metabolism, Ajou University School of Medicine, Suwon, Korea. djkim@ajou.ac.kr.ORCID http://orcid.org/0000-0003-1025-2044

Funding

UC Davis Clinical and Translational Science CenterUL1TR000002 · NCATS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BERGLUND, LARS F · 2012 to 2015
$12.2M
NCATS NIH HHS UL1 TR000002
6 · The paper itself

Abstract

A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.

Indexed as

Clinical prediction modelClinical usefulnessDevelopmentValidation

Identifiers

PMID26996421
PMCPMC4803559

What OpenQuestion holds

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LicenceCC BY-NC
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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.