Literature DB >> 27362778

Key steps and common pitfalls in developing and validating risk models.

L Wynants1, G S Collins2, B Van Calster3.   

Abstract

Models for estimating an individual's risk of having or developing a disease are abundant in the medical literature, yet many do not meet the methodological standards that have been set to maximise generalisability and utility. This paper presents an overview of ten steps from the conception of the study to the implementation of the risk model and discusses common pitfalls. We discuss crucial aspects of study design, data collection, model development and performance evaluation, and discuss how to bring the model to clinical practice. TWEETABLE ABSTRACT: We present an overview of ten key steps for the development of risk models and discuss common pitfalls.
© 2016 Royal College of Obstetricians and Gynaecologists.

Keywords:  Clinical prediction model; logistic regression; model development; model reporting; model validation; risk model

Mesh:

Year:  2016        PMID: 27362778     DOI: 10.1111/1471-0528.14170

Source DB:  PubMed          Journal:  BJOG        ISSN: 1470-0328            Impact factor:   6.531


  19 in total

1.  Concerns about Race and Ethnicity within the United States Fracture Risk Assessment Tool.

Authors:  Martin Mayer; Jon Keevil; Karen E Hansen
Journal:  J Bone Metab       Date:  2022-05-31

2.  Risk of major postoperative complications in breast reconstructive surgery with and without an acellular dermal matrix: A development of a prognostic prediction model.

Authors:  N S Hillberg; J Hogenboom; J Hommes; S M J Van Kuijk; X H A Keuter; R R W J van der Hulst
Journal:  JPRAS Open       Date:  2022-05-12

3.  Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care.

Authors:  Garrett S Bullock; Tom Hughes; Amelia H Arundale; Patrick Ward; Gary S Collins; Stefan Kluzek
Journal:  Sports Med       Date:  2022-02-17       Impact factor: 11.928

Review 4.  Tutorial: a guide to performing polygenic risk score analyses.

Authors:  Shing Wan Choi; Timothy Shin-Heng Mak; Paul F O'Reilly
Journal:  Nat Protoc       Date:  2020-07-24       Impact factor: 13.491

5.  Risk prediction models for discrete ordinal outcomes: Calibration and the impact of the proportional odds assumption.

Authors:  Michael Edlinger; Maarten van Smeden; Hannes F Alber; Maria Wanitschek; Ben Van Calster
Journal:  Stat Med       Date:  2021-12-12       Impact factor: 2.497

6.  Individual participant data validation of the PICNICC prediction model for febrile neutropenia.

Authors:  Bob Phillips; Jessica Elizabeth Morgan; Gabrielle M Haeusler; Richard D Riley
Journal:  Arch Dis Child       Date:  2019-11-05       Impact factor: 3.791

7.  Predictors for major cardiovascular outcomes in stable ischaemic heart disease (PREMAC): statistical analysis plan for data originating from the CLARICOR (clarithromycin for patients with stable coronary heart disease) trial.

Authors:  Per Winkel; Janus Christian Jakobsen; Jørgen Hilden; Theis Lange; Gorm Boje Jensen; Erik Kjøller; Ahmad Sajadieh; Jens Kastrup; Hans Jørn Kolmos; Anders Larsson; Johan Ärnlöv; Christian Gluud
Journal:  Diagn Progn Res       Date:  2017-03-29

8.  Cardiovascular Disease Prognostic Models in Latin America and the Caribbean: A Systematic Review.

Authors:  Rodrigo M Carrillo-Larco; Carlos Altez-Fernandez; Niels Pacheco-Barrios; Claudia Bambs; Vilma Irazola; J Jaime Miranda; Goodarz Danaei; Pablo Perel
Journal:  Glob Heart       Date:  2019-03

Review 9.  Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature.

Authors:  Laura E Cowley; Daniel M Farewell; Sabine Maguire; Alison M Kemp
Journal:  Diagn Progn Res       Date:  2019-08-22

10.  Risk scores for type 2 diabetes mellitus in Latin America: a systematic review of population-based studies.

Authors:  R M Carrillo-Larco; D J Aparcana-Granda; J R Mejia; N C Barengo; A Bernabe-Ortiz
Journal:  Diabet Med       Date:  2019-09-06       Impact factor: 4.359

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