Comparing risk prediction models.

Comparing risk prediction models.

Xehetasun bibliografikoak
Egile Nagusiak:	Collins, G, Moons, K
Formatua:	Journal article
Hizkuntza:	English
Argitaratua:	2012

Antzeko izenburuak

Comparing risk prediction models: Should be routine when deriving a new model for the same purpose
nork: Collins, G, et al.
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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.
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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
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Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.
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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) : the TRIPOD statement
nork: Collins, G, et al.
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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) : the TRIPOD statement
nork: Collins, G, et al.
Argitaratua: (2015)

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement.
nork: Collins, G, et al.
Argitaratua: (2015)

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.
nork: Collins, G, et al.
Argitaratua: (2015)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement
nork: Collins, G, et al.
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PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration
nork: Moons, K, et al.
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Cardiovascular disease risk prediction in the UK: Which is the best risk prediction model?
nork: Collins, G
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PROBAST: A tool to assess the risk of bias and applicability of prediction model studies
nork: Wolff, RF, et al.
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Risk prediction models: II. External validation, model updating, and impact assessment.
nork: Moons, K, et al.
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Risk prediction models: II. External validation, model updating, and impact assessment
nork: Moons, K, et al.
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Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes
nork: Riley, R, et al.
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A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
nork: Debray, T, et al.
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Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
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External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges
nork: Riley, R, et al.
Argitaratua: (2016)

Interpretation of CVD risk predictions in clinical practice: Mission impossible?
nork: G R Lagerweij, et al.
Argitaratua: (2019-01-01)

Prediction models for cardiovascular disease risk in the general population: systematic review
nork: Damen, J, et al.
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Sample size for binary logistic prediction models: Beyond events per variable criteria
nork: Van Smeden, M, et al.
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Critical appraisal and data extraction for systematic reviews of prediction modelling studies : the CHARMS checklist
nork: Moons, K, et al.
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Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
nork: Andaur Navarro, CL, et al.
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Multivariable risk prediction models: it's all about the performance.
nork: Collins, G, et al.
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Statistical Primer: Developing and validating a risk prediction model
nork: Grant, S, et al.
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A new community for those involved and interested in diagnosis and prognosis
nork: Moons, K, et al.
Argitaratua: (2017)

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration.
nork: Moons, K, et al.
Argitaratua: (2015)

Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker
nork: Moons, K, et al.
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Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker.
nork: Moons, K, et al.
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Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies
nork: Heus, P, et al.
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Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement
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Effect of tailoring anticoagulant treatment duration by applying a recurrence risk prediction model in patients with venous thromboembolism compared to usual care: A randomized controlled trial.
nork: Geert-Jan Geersing, et al.
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Calculating the sample size required for developing a clinical prediction model
nork: Riley, RD, et al.
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Transparent reporting of multivariable prediction models in journal and conference abstracts: TRIPOD for abstracts
nork: Heus, P, et al.
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Development and validation of a prediction model with missing predictor data: a practical approach.
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Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease
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Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist
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External validation of multivariable prediction models : a systematic review of methodological conduct and reporting
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Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration
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