Risk of bias of prognostic models developed using machine learning: a systematic review in oncology

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology

<strong>Background<br></strong> Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain....

書目詳細資料
Main Authors:	Dhiman, P, Ma, J, Andaur Navarro, CL, Speich, B, Bullock, G, Damen, JAA, Hooft, L, Kirtley, S, Riley, RD, Van Calster, B, Moons, KGM, Collins, GS
格式:	Journal article
語言:	English
出版:	BioMed Central 2022

相似書籍

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review
由: Dhiman, P, et al.
出版: (2022)

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

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

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology
由: Paula Dhiman, et al.
出版: (2022-07-01)

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

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review
由: Paula Dhiman, et al.
出版: (2022-04-01)

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

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

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

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

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
由: Collins, GS, et al.
出版: (2021)

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

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

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

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

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning
由: Collins, GS, et al.
出版: (2023)

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
由: Gary S Collins, et al.
出版: (2021-07-01)

Evaluation of clinical prediction models (part 1): from development to external validation
由: Collins, GS, et al.
出版: (2024)

PROBAST: A tool to assess the risk of bias and applicability of prediction model studies
由: Wolff, RF, et al.
出版: (2019)

On the aggregation of published prognostic scores for causal inference in observational studies
由: Nguyen, T-L, et al.
出版: (2020)

A note on estimating the Cox-Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome
由: Riley, RD, et al.
出版: (2020)

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

Overinterpretation and misreporting of prognostic factor studies in oncology: a systematic review
由: Kempf, E, et al.
出版: (2018)

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

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques
由: Gary S Collins, et al.
出版: (2020-11-01)

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review
由: Constanza L. Andaur Navarro, et al.
出版: (2022-01-01)

Methodology over metrics: current scientific standards are a disservice to patients and society
由: Van Calster, B, et al.
出版: (2021)

TRIPOD statement: a preliminary pre-post analysis of reporting and methods of prediction models
由: Zamanipoor Najafabadi, AH, et al.
出版: (2020)

Reporting guidelines used varying methodology to develop recommendations
由: Schlussel, MM, et al.
出版: (2023)

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review
由: Dhiman, P, et al.
出版: (2023)

A systematic review showed more consideration is needed when conducting nonrandomized studies of interventions.
由: Dhiman, P, et al.
出版: (2019)

Overinterpretation and misreporting of prognostic factor studies in medical oncology
由: Kempf, E, et al.
出版: (2016)

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review
由: Ma, J, et al.
出版: (2023)

Regarding a nomogram to predict osteoradionecrosis in oral cancer after marginal mandibulectomy and radiotherapy
由: Collins, GS, et al.
出版: (2019)

Artificial intelligence in lung cancer diagnostic imaging: a review of the reporting and conduct of research published 2018–2019
由: Logullo, P, et al.
出版: (2023)

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

A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
由: Debray, T, et al.
出版: (2018)

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal
由: Wynants, L, et al.
出版: (2020)

Minimum sample size for developing a multivariable prediction model using multinomial logistic regression
由: Pate, A, et al.
出版: (2023)

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