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....

وصف كامل

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	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
التنسيق:	Journal article
اللغة:	English
منشور في:	BioMed Central 2022

مواد مشابهة

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review
حسب: Dhiman, P, وآخرون
منشور في: (2022)

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review
حسب: Dhiman, P, وآخرون
منشور في: (2023)

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved
حسب: Dhiman, P, وآخرون
منشور في: (2021)

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology
حسب: Paula Dhiman, وآخرون
منشور في: (2022-07-01)

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
حسب: Andaur Navarro, CL, وآخرون
منشور في: (2021)

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review
حسب: Paula Dhiman, وآخرون
منشور في: (2022-04-01)

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review
حسب: Andaur Navarro, CL, وآخرون
منشور في: (2022)

Systematic review finds "spin" practices and poor reporting standards in studies on machine learning-based prediction models
حسب: Andaur Navarro, CL, وآخرون
منشور في: (2023)

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models
حسب: Andaur Navarro, CL, وآخرون
منشور في: (2022)

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed
حسب: Levis, B, وآخرون
منشور في: (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
حسب: Collins, GS, وآخرون
منشور في: (2021)

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)
حسب: Snell, KIE, وآخرون
منشور في: (2023)

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques
حسب: Andaur Navarro, CL, وآخرون
منشور في: (2020)

Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist
حسب: Debray, TPA, وآخرون
منشور في: (2023)

Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration
حسب: Debray, TPA, وآخرون
منشور في: (2023)

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning
حسب: Collins, GS, وآخرون
منشور في: (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
حسب: Gary S Collins, وآخرون
منشور في: (2021-07-01)

Evaluation of clinical prediction models (part 1): from development to external validation
حسب: Collins, GS, وآخرون
منشور في: (2024)

PROBAST: A tool to assess the risk of bias and applicability of prediction model studies
حسب: Wolff, RF, وآخرون
منشور في: (2019)

On the aggregation of published prognostic scores for causal inference in observational studies
حسب: Nguyen, T-L, وآخرون
منشور في: (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
حسب: Riley, RD, وآخرون
منشور في: (2020)

Transparent reporting of multivariable prediction models in journal and conference abstracts: TRIPOD for abstracts
حسب: Heus, P, وآخرون
منشور في: (2020)

Overinterpretation and misreporting of prognostic factor studies in oncology: a systematic review
حسب: Kempf, E, وآخرون
منشور في: (2018)

Calculating the power of a planned individual participant data meta‐analysis to examine prognostic factor effects for a binary outcome
حسب: Whittle, R, وآخرون
منشور في: (2024)

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques
حسب: Gary S Collins, وآخرون
منشور في: (2020-11-01)

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review
حسب: Constanza L. Andaur Navarro, وآخرون
منشور في: (2022-01-01)

Methodology over metrics: current scientific standards are a disservice to patients and society
حسب: Van Calster, B, وآخرون
منشور في: (2021)

TRIPOD statement: a preliminary pre-post analysis of reporting and methods of prediction models
حسب: Zamanipoor Najafabadi, AH, وآخرون
منشور في: (2020)

Reporting guidelines used varying methodology to develop recommendations
حسب: Schlussel, MM, وآخرون
منشور في: (2023)

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review
حسب: Dhiman, P, وآخرون
منشور في: (2023)

A systematic review showed more consideration is needed when conducting nonrandomized studies of interventions.
حسب: Dhiman, P, وآخرون
منشور في: (2019)

Overinterpretation and misreporting of prognostic factor studies in medical oncology
حسب: Kempf, E, وآخرون
منشور في: (2016)

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review
حسب: Ma, J, وآخرون
منشور في: (2023)

Regarding a nomogram to predict osteoradionecrosis in oral cancer after marginal mandibulectomy and radiotherapy
حسب: Collins, GS, وآخرون
منشور في: (2019)

Artificial intelligence in lung cancer diagnostic imaging: a review of the reporting and conduct of research published 2018–2019
حسب: Logullo, P, وآخرون
منشور في: (2023)

A guide to systematic review and meta-analysis of prognostic factor studies
حسب: Riley, R, وآخرون
منشور في: (2019)

A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
حسب: Debray, T, وآخرون
منشور في: (2018)

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal
حسب: Wynants, L, وآخرون
منشور في: (2020)

Minimum sample size for developing a multivariable prediction model using multinomial logistic regression
حسب: Pate, A, وآخرون
منشور في: (2023)

Calculating the sample size required for developing a clinical prediction model
حسب: Riley, RD, وآخرون
منشور في: (2020)