A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes

A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes

It is widely recommended that any developed—diagnostic or prognostic—prediction model is externally validated in terms of its predictive performance measured by calibration and discrimination. When multiple validations have been performed, a systematic review followed by a formal meta-analysis helps...

תיאור מלא

מידע ביבליוגרפי
Main Authors:	Debray, T, Damen, J, Riley, R, Snell, K, Reitsma, J, Hooft, L, Collins, G, Moons, K
פורמט:	Journal article
שפה:	English
יצא לאור:	SAGE Publications 2018

פריטים דומים

Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes
מאת: Riley, R, et al.
יצא לאור: (2018)

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)
מאת: Snell, KIE, et al.
יצא לאור: (2023)

A guide to systematic review and meta-analysis of prognostic factor studies
מאת: Riley, R, et al.
יצא לאור: (2019)

External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges
מאת: Riley, R, et al.
יצא לאור: (2016)

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

Sample size for binary logistic prediction models: Beyond events per variable criteria
מאת: Van Smeden, M, et al.
יצא לאור: (2018)

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

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

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed
מאת: Levis, B, et al.
יצא לאור: (2023)

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.
מאת: van Smeden, M, et al.
יצא לאור: (2016)

Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis
מאת: Johanna A. Damen, et al.
יצא לאור: (2019-06-01)

Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement
מאת: Heus, P, et al.
יצא לאור: (2018)

Uniformity in measuring adherence to reporting guidelines: the example of TRIPOD for assessing completeness of reporting of prediction model studies
מאת: Heus, P, et al.
יצא לאור: (2019)

Transparent reporting of multivariable prediction models in journal and conference abstracts: TRIPOD for abstracts
מאת: Heus, P, et al.
יצא לאור: (2020)

Minimum sample size for developing a multivariable prediction model: Part I – Continuous outcomes
מאת: Riley, R, et al.
יצא לאור: (2018)

Explicit inclusion of treatment in prognostic modelling was recommended in observational and randomised settings
מאת: Groenwold, R, et al.
יצא לאור: (2016)

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis
מאת: Maarten van Smeden, et al.
יצא לאור: (2016-11-01)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement.
מאת: Collins, G, et al.
יצא לאור: (2015)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement
מאת: Collins, G, et al.
יצא לאור: (2015)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
מאת: Collins, G, et al.
יצא לאור: (2015)

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.
מאת: Collins, G, et al.
יצא לאור: (2014)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) : the TRIPOD statement
מאת: Collins, G, et al.
יצא לאור: (2015)

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) : the TRIPOD statement
מאת: Collins, G, et al.
יצא לאור: (2015)

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

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

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

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

PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration
מאת: Moons, K, et al.
יצא לאור: (2019)

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review
מאת: Andaur Navarro, CL, et al.
יצא לאור: (2022)

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
מאת: Andaur Navarro, CL, et al.
יצא לאור: (2021)

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques
מאת: Andaur Navarro, CL, et al.
יצא לאור: (2020)

Systematic review finds "spin" practices and poor reporting standards in studies on machine learning-based prediction models
מאת: Andaur Navarro, CL, et al.
יצא לאור: (2023)

Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement
מאת: Pauline Heus, et al.
יצא לאור: (2018-07-01)

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved
מאת: Dhiman, P, et al.
יצא לאור: (2021)

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

Calculating the power of a planned individual participant data meta‐analysis to examine prognostic factor effects for a binary outcome
מאת: Whittle, R, et al.
יצא לאור: (2024)

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review
מאת: Dhiman, P, et al.
יצא לאור: (2023)

Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome
מאת: Riley, RD, et al.
יצא לאור: (2022)

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

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