Cargando…

Adaptive sample size determination for the development of clinical prediction models

BACKGROUND: We suggest an adaptive sample size calculation method for developing clinical prediction models, in which model performance is monitored sequentially as new data comes in. METHODS: We illustrate the approach using data for the diagnosis of ovarian cancer (n = 5914, 33% event fraction) an...

Descripción completa

Detalles Bibliográficos
Autores principales:	Christodoulou, Evangelia, van Smeden, Maarten, Edlinger, Michael, Timmerman, Dirk, Wanitschek, Maria, Steyerberg, Ewout W., Van Calster, Ben
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2021
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7983402/ https://www.ncbi.nlm.nih.gov/pubmed/33745449 http://dx.doi.org/10.1186/s41512-021-00096-5

Ejemplares similares

Sample size considerations and predictive performance of multinomial logistic prediction models
por: de Jong, Valentijn M. T., et al.
Publicado: (2019)

Risk prediction models for discrete ordinal outcomes: Calibration and the impact of the proportional odds assumption
por: Edlinger, Michael, et al.
Publicado: (2021)

Three myths about risk thresholds for prediction models
por: Wynants, Laure, et al.
Publicado: (2019)

There is no such thing as a validated prediction model
por: Van Calster, Ben, et al.
Publicado: (2023)

Calibration: the Achilles heel of predictive analytics
por: Van Calster, Ben, et al.
Publicado: (2019)

The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
por: van den Goorbergh, Ruben, et al.
Publicado: (2022)

Predictive analytics in health care: how can we know it works?
por: Van Calster, Ben, et al.
Publicado: (2019)

External validation and extension of a diagnostic model for obstructive coronary artery disease: a cross-sectional predictive evaluation in 4888 patients of the Austrian Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort
por: Edlinger, Michael, et al.
Publicado: (2017)

Minimum sample size for developing a multivariable prediction model using multinomial logistic regression
por: Pate, Alexander, et al.
Publicado: (2023)

Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests
por: Vickers, Andrew J, et al.
Publicado: (2016)

Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic review
por: Hudda, Mohammed T., et al.
Publicado: (2023)

Developing risk models for multicenter data using standard logistic regression produced suboptimal predictions: A simulation study
por: Falconieri, Nora, et al.
Publicado: (2020)

Cohort profile: the Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort
por: Wanitschek, Maria, et al.
Publicado: (2018)

A simple, step-by-step guide to interpreting decision curve analysis
por: Vickers, Andrew J., et al.
Publicado: (2019)

Impact of predictor measurement heterogeneity across settings on the performance of prediction models: A measurement error perspective
por: Luijken, K., et al.
Publicado: (2019)

Commentary: Artificial Intelligence and Statistics: Just the Old Wine in New Wineskins?
por: de Hond, Anne A. H., et al.
Publicado: (2022)

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

Methodology over metrics: current scientific standards are a disservice to patients and society
por: Van Calster, Ben, et al.
Publicado: (2021)

The calibrated model-based concordance improved assessment of discriminative ability in patient clusters of limited sample size
por: van Klaveren, David, et al.
Publicado: (2019)

Polytomous diagnosis of ovarian tumors as benign, borderline, primary invasive or metastatic: development and validation of standard and kernel-based risk prediction models
por: Van Calster, Ben, et al.
Publicado: (2010)

Perspectives on validation of clinical predictive algorithms
por: de Hond, Anne A. H., et al.
Publicado: (2023)

Sample size for binary logistic prediction models: Beyond events per variable criteria
por: van Smeden, Maarten, et al.
Publicado: (2018)

Screening for data clustering in multicenter studies: the residual intraclass correlation
por: Wynants, Laure, et al.
Publicado: (2013)

Clinical prediction models: a practical approach to development, validation, and updating
por: Steyerberg, Ewout W
Publicado: (2019)

Clinical prediction models: a practical approach to development, validation, and updating
por: Steyerberg, Ewout W
Publicado: (2009)

Validation and updating of risk models based on multinomial logistic regression
por: Van Calster, Ben, et al.
Publicado: (2017)

Clinical prediction models : a practical approach to development, validation, and updating /
por: Steyerberg, Ewout W.
Publicado: (2009)

Estimation of required sample size for external validation of risk models for binary outcomes
por: Pavlou, Menelaos, et al.
Publicado: (2021)

External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature
por: Wessler, Benjamin S., et al.
Publicado: (2021)

Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm
por: Ledger, Ashleigh, et al.
Publicado: (2023)

The impact of complaints procedures on the welfare, health and clinical practise of 7926 doctors in the UK: a cross-sectional survey
por: Bourne, Tom, et al.
Publicado: (2015)

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
por: Collins, Gary S, et al.
Publicado: (2021)

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease( )
por: van Smeden, Maarten, et al.
Publicado: (2022)

Feature selection and validated predictive performance in the domain of Legionella pneumophila: a comparative study
por: van der Ploeg, Tjeerd, et al.
Publicado: (2016)

Generalizability of Cardiovascular Disease Clinical Prediction Models: 158 Independent External Validations of 104 Unique Models
por: Gulati, Gaurav, et al.
Publicado: (2022)

Visualizing Risk Prediction Models
por: Van Belle, Vanya, et al.
Publicado: (2015)

Risk calculation charts for multiclass prediction models
por: Van Belle, Vanya, et al.
Publicado: (2015)

Assessing Discriminative Performance at External Validation of Clinical Prediction Models
por: Nieboer, Daan, et al.
Publicado: (2016)

Sample size determination with a pilot study
por: O’Neill, Ben
Publicado: (2022)

Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models
por: Austin, Peter C, et al.
Publicado: (2014)

Cannot write session to /tmp/vufind_sessions/sess_s3pkgo9sbee2q2so7uuguh7ugp