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Rombach, I., Gray, A.M., Jenkinson, C., Murray, D.W. and Rivero-Arias, O. (2018) Multiple Imputation for Patient Reported Outcome Measures in Randomised Controlled Trials: Advantages and Disadvantages of Imputing at the Item, Subscale or Composite Score Level. BMC Medical Research Methodology, 18, Article No. 87.
https://doi.org/10.1186/s12874-018-0542-6

has been cited by the following article:

• TITLE: Missing Data Imputation: A Comprehensive Review

  AUTHORS: Majed Alwateer, El-Sayed Atlam, Mahmoud Mohammed Abd El-Raouf, Osama A. Ghoneim, Ibrahim Gad

  KEYWORDS: Missing Data, Machine Learning, Prediction, Deep Learning, Imputation

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.11, November 11, 2024

  ABSTRACT: Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.