Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

<strong>Background<br></strong> Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. <br><strong> Methods<br></strong> We conducted a systematic review in MEDLINE and Embase be...

Full beskrivning

Bibliografiska uppgifter
Huvudupphovsmän:	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
Materialtyp:	Journal article
Språk:	English
Publicerad:	BioMed Central 2022

Liknande verk

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology
av: Dhiman, P, et al.
Publicerad: (2022)

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

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

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

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

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

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

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

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

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

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

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed
av: Levis, B, et al.
Publicerad: (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
av: Collins, GS, et al.
Publicerad: (2021)

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

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

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

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

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

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

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
av: Gary S Collins, et al.
Publicerad: (2021-07-01)

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

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

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

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

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

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

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

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

More consideration is needed when conducting non-randomised studies of interventions
av: Dhiman, P, et al.
Publicerad: (2019)

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

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

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

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

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

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

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

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

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

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