Flaws in the development and validation of a covid-19 prediction model

Flaws in the development and validation of a covid-19 prediction model

Bibliografske podrobnosti
Main Authors:	Collins, GS, Riley, RD, van Smeden, M
Format:	Journal article
Jezik:	English
Izdano:	Oxford University Press 2020

Podobne knjige/članki

COVID-19 prediction models should adhere to methodological and reporting standards
od: Collins, GS, et al.
Izdano: (2020)

Minimum sample size for external validation of a clinical prediction model with a binary outcome
od: Riley, RD, et al.
Izdano: (2021)

Statistical flaws in the development of a prediction model.
od: Collins, G
Izdano: (2015)

Evaluation of clinical prediction models (part 1): from development to external validation
od: Collins, GS, et al.
Izdano: (2024)

Flawed validation of FRAX
od: Collins, G, et al.
Izdano: (2016)

Minimum sample size for developing a multivariable prediction model using multinomial logistic regression
od: Pate, A, et al.
Izdano: (2023)

Targeted validation: validating clinical prediction models in their intended population and setting
od: Sperrin, M, et al.
Izdano: (2022)

There are no shortcuts in the development and validation of a COVID‐19 prediction model
od: Collins, GS, et al.
Izdano: (2020)

A prediction model for venous thromboembolism in the intensive care unit: flawed methods may lead to inaccurate predictions
od: Gerry, S, et al.
Izdano: (2023)

Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome
od: Riley, RD, et al.
Izdano: (2021)

Stability of clinical prediction models developed using statistical or machine learning methods
od: Riley, RD, et al.
Izdano: (2023)

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review
od: Ma, J, et al.
Izdano: (2023)

Methodology over metrics: current scientific standards are a disservice to patients and society
od: Van Calster, B, et al.
Izdano: (2021)

Flawed external validation study of the ADNEX model to diagnose ovarian cancer.
od: Van Calster, B, et al.
Izdano: (2016)

Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist
od: Debray, TPA, et al.
Izdano: (2023)

A note on estimating the Cox-Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome
od: Riley, RD, et al.
Izdano: (2020)

Statistical issues in the development of COVID‐19 prediction models
od: Collins, GS, et al.
Izdano: (2020)

Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration
od: Debray, TPA, et al.
Izdano: (2023)

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal
od: Wynants, L, et al.
Izdano: (2020)

Calculating the sample size required for developing a clinical prediction model
od: Riley, RD, et al.
Izdano: (2020)

Developing clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive performance
od: Martin, GP, et al.
Izdano: (2021)

Minimum sample size for external validation of a clinical prediction model with a continuous outcome
od: Archer, L, et al.
Izdano: (2020)

There is no such thing as a validated prediction model
od: Ben Van Calster, et al.
Izdano: (2023-02-01)

Cardiovascular vulnerability predicts hospitalisation in primary care clinically suspected and confirmed COVID-19 patients: A model development and validation study.
od: Florien S van Royen, et al.
Izdano: (2022-01-01)

Cardiovascular vulnerability predicts hospitalisation in primary care clinically suspected and confirmed COVID-19 patients: A model development and validation study
od: Florien S. van Royen, et al.
Izdano: (2022-01-01)

Evaluation of clinical prediction models (part 2): how to undertake an external validation study
od: Riley, RD, et al.
Izdano: (2024)

Design flaws in EuroSCORE II.
od: Collins, G, et al.
Izdano: (2013)

FLAWED /
od: Ahern, Cecelia, 1981-, author 319498
Izdano: (2016)

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models
od: Andaur Navarro, CL, et al.
Izdano: (2022)

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence
od: Collins, GS, et al.
Izdano: (2021)

Trustworthy or flawed clinical prediction rule?
od: Anders Granholm, et al.
Izdano: (2018-02-01)

Clinical prediction models and the multiverse of madness
od: Riley, RD, et al.
Izdano: (2023)

External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb
od: Snell, KIE, et al.
Izdano: (2021)

Statistical flaws of the fitness-fatigue sports performance prediction model
od: Alexandre Marchal, et al.
Izdano: (2025-01-01)

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review
od: Dhiman, P, et al.
Izdano: (2023)

Born to fail: flaws in replication design produce intended results
od: Abraham D. Flaxman, et al.
Izdano: (2020-03-01)

Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?
od: Jenkins, DA, et al.
Izdano: (2021)

Risk, Trust, and Flawed Assumptions: Vaccine Hesitancy During the COVID-19 Pandemic
od: Omid V. Ebrahimi, et al.
Izdano: (2021-07-01)

A flawed trial
od: Abd Razak, Dzulkifli
Izdano: (2007)

Flaw Characteristics of Architectural Glass and Long-Term Strength Prediction Model
od: Siyi Yi, et al.
Izdano: (2024-06-01)