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Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement

European urology, 2015-06, Vol.67 (6), p.1142-1151

2015

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Barriers and facilitators perceived by physicians when using prediction models in practice

Journal of clinical epidemiology, 2016-02, Vol.70, p.136-145

2016

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Internal and external validation of predictive models: A simulation study of bias and precision in small samples

Journal of clinical epidemiology, 2003-05, Vol.56 (5), p.441-447

2003

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Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement

Annals of internal medicine, 2015-01, Vol.162 (1), p.55-63

2015

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PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

Annals of internal medicine, 2019-01, Vol.170 (1), p.W1

2019

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PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Annals of internal medicine, 2019-01, Vol.170 (1), p.51

2019

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Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist

PLoS medicine, 2014-10, Vol.11 (10), p.e1001744-e1001744

2014

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Prognosis Research Strategy (PROGRESS) 3: prognostic model research

PLoS medicine, 2013-02, Vol.10 (2), p.e1001381-e1001381

2013

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Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

Annals of internal medicine, 2015-01, Vol.162 (1), p.W1-W73

2015

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Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement

Journal of clinical epidemiology, 2015-02, Vol.68 (2), p.112-121

2015

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Prognosis Research Strategy (PROGRESS) 2: prognostic factor research

PLoS medicine, 2013-02, Vol.10 (2), p.e1001380-e1001380

2013

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Reporting and methods in clinical prediction research: a systematic review

PLoS medicine, 2012-05, Vol.9 (5), p.1-12

2012

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Prognostic factors for medically intractable epilepsy: A systematic review

Epilepsy research, 2013-10, Vol.106 (3), p.301-310

2013

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Using evidence to combat overdiagnosis and overtreatment: evaluating treatments, tests, and disease definitions in the time of too much

PLoS medicine, 2014-07, Vol.11 (7), p.e1001655-e1001655

2014

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Missing covariate data in medical research: To impute is better than to ignore

Journal of clinical epidemiology, 2010-07, Vol.63 (7), p.721-727

2010

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Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis

Canadian Medical Association journal (CMAJ), 2012-08, Vol.184 (11), p.1265-1269

2012

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Review: A gentle introduction to imputation of missing values

Journal of clinical epidemiology, 2006-10, Vol.59 (10), p.1087-1091

2006

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A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis

Statistics in medicine, 2013-08, Vol.32 (18), p.3158-3180

2013

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Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use

PLoS medicine, 2015-10, Vol.12 (10), p.e1001886-e1001886

2015

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Improving the transparency of prognosis research: the role of reporting, data sharing, registration, and protocols

PLoS medicine, 2014-07, Vol.11 (7), p.e1001671

2014

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Randomized trials with missing outcome data: how to analyze and what to report

Canadian Medical Association journal (CMAJ), 2014-10, Vol.186 (15), p.1153-1157

2014

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Bias in Sensitivity and Specificity Caused by Data-Driven Selection of Optimal Cutoff Values: Mechanisms, Magnitude, and Solutions

Clinical chemistry (Baltimore, Md.), 2008-04, Vol.54 (4), p.729-737

2008

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Individual participant data meta-analyses should not ignore clustering

Journal of clinical epidemiology, 2013-08, Vol.66 (8), p.865-873.e4

2013

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Evidence synthesis in prognosis research

Diagnostic and prognostic research, 2019-07, Vol.3 (1), p.13-13, Article 13

2019

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Use of expert panels to define the reference standard in diagnostic research: a systematic review of published methods and reporting

PLoS medicine, 2013-10, Vol.10 (10), p.e1001531-e1001531

2013

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Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement

Barriers and facilitators perceived by physicians when using prediction models in practice

Internal and external validation of predictive models: A simulation study of bias and precision in small samples

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement

PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist

Prognosis Research Strategy (PROGRESS) 3: prognostic model research

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement

Prognosis Research Strategy (PROGRESS) 2: prognostic factor research

Reporting and methods in clinical prediction research: a systematic review

Prognostic factors for medically intractable epilepsy: A systematic review

Using evidence to combat overdiagnosis and overtreatment: evaluating treatments, tests, and disease definitions in the time of too much

Missing covariate data in medical research: To impute is better than to ignore

Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis

Review: A gentle introduction to imputation of missing values

A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis

Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use

Improving the transparency of prognosis research: the role of reporting, data sharing, registration, and protocols

Randomized trials with missing outcome data: how to analyze and what to report

Bias in Sensitivity and Specificity Caused by Data-Driven Selection of Optimal Cutoff Values: Mechanisms, Magnitude, and Solutions

Individual participant data meta-analyses should not ignore clustering

Evidence synthesis in prognosis research

Use of expert panels to define the reference standard in diagnostic research: a systematic review of published methods and reporting

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