Comparing risk prediction models.

Comparing risk prediction models.

Bibliografski detalji
Glavni autori:	Collins, G, Moons, K
Format:	Journal article
Jezik:	English
Izdano:	2012

Slični predmeti

Comparing risk prediction models: Should be routine when deriving a new model for the same purpose
od: Collins, G, i dr.
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Reporting of artificial intelligence prediction models
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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.
od: Collins, G, i dr.
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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
od: Collins, G, i dr.
Izdano: (2015)

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.
od: Collins, G, i dr.
Izdano: (2014)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) : the TRIPOD statement
od: Collins, G, i dr.
Izdano: (2015)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) : the TRIPOD statement
od: Collins, G, i dr.
Izdano: (2015)

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement.
od: Collins, G, i dr.
Izdano: (2015)

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.
od: Collins, G, i dr.
Izdano: (2015)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement
od: Collins, G, i dr.
Izdano: (2015)

PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration
od: Moons, K, i dr.
Izdano: (2019)

Cardiovascular disease risk prediction in the UK: Which is the best risk prediction model?
od: Collins, G
Izdano: (2013)

PROBAST: A tool to assess the risk of bias and applicability of prediction model studies
od: Wolff, RF, i dr.
Izdano: (2019)

Risk prediction models: II. External validation, model updating, and impact assessment.
od: Moons, K, i dr.
Izdano: (2012)

Risk prediction models: II. External validation, model updating, and impact assessment
od: Moons, K, i dr.
Izdano: (2012)

Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes
od: Riley, R, i dr.
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A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
od: Debray, T, i dr.
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Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
od: Riley, R, i dr.
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External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges
od: Riley, R, i dr.
Izdano: (2016)

Interpretation of CVD risk predictions in clinical practice: Mission impossible?
od: G R Lagerweij, i dr.
Izdano: (2019-01-01)

Prediction models for cardiovascular disease risk in the general population: systematic review
od: Damen, J, i dr.
Izdano: (2016)

Sample size for binary logistic prediction models: Beyond events per variable criteria
od: Van Smeden, M, i dr.
Izdano: (2018)

Critical appraisal and data extraction for systematic reviews of prediction modelling studies : the CHARMS checklist
od: Moons, K, i dr.
Izdano: (2014)

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
od: Andaur Navarro, CL, i dr.
Izdano: (2021)

Multivariable risk prediction models: it's all about the performance.
od: Collins, G, i dr.
Izdano: (2013)

Statistical Primer: Developing and validating a risk prediction model
od: Grant, S, i dr.
Izdano: (2018)

A new community for those involved and interested in diagnosis and prognosis
od: Moons, K, i dr.
Izdano: (2017)

Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker
od: Moons, K, i dr.
Izdano: (2012)

Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker.
od: Moons, K, i dr.
Izdano: (2012)

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration.
od: Moons, K, i dr.
Izdano: (2015)

Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies
od: Heus, P, i dr.
Izdano: (2019)

Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement
od: Heus, P, i dr.
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Calculating the sample size required for developing a clinical prediction model
od: Riley, RD, i dr.
Izdano: (2020)

Transparent reporting of multivariable prediction models in journal and conference abstracts: TRIPOD for abstracts
od: Heus, P, i dr.
Izdano: (2020)

Development and validation of a prediction model with missing predictor data: a practical approach.
od: Vergouwe, Y, i dr.
Izdano: (2010)

Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease
od: Bertens LCM, i dr.
Izdano: (2013-10-01)

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

External validation of multivariable prediction models : a systematic review of methodological conduct and reporting
od: Collins, G, i dr.
Izdano: (2014)

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

Comparative performance of cardiovascular risk prediction models in people living with HIV
od: Irtiza S. Tahir, i dr.
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