Handling missing data and measurement error for early-onset myopia risk prediction models.

Journal: BMC medical research methodology

PMID: 39243025

Abstract

BACKGROUND: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction modelling that can introduce bias in myopia prediction.

Authors

Hongyu Lai

/ （ 610041） West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China.
Kaiye Gao

School of Economics and Management, Beijing Forestry University, Beijing, China.
Meiyan Li

Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
Tao Li

Department of Emergency Medicine, Jining No.1 People's Hospital, Jining, China.
Xiaodong Zhou

Institute of Materials Research Engineering, A*STAR (Agency for Science, Technology and Research).
Xingtao Zhou

Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
Hui Guo

Health Sciences and Innovation, Surrey Memorial Hospital, Fraser Health Authority, Surrey, BC, Canada.
Bo Fu

Keywords

Age of Onset Bayes Theorem Child China Cohort Studies Female Humans Logistic Models Machine Learning Male Models, Statistical Myopia Risk Assessment Risk Factors ROC Curve

External Resources

View on PubMed Access via DOI PubMed (39243025)

Handling missing data and measurement error for early-onset myopia risk prediction models.

Abstract

Authors

Keywords

External Resources

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