ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2026
[Comparison of missing data handling methods for AC
Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesTo explore the impact of different missing data handling methods on AC
methodsMonte Carlo simulation was used to generate evaluation data under different missing mechanisms. The parameters generated included the number of raters, categories, sample size, disease prevalence, random rating probability, and missing proportion. Four missing data handling methods, by excluding subjects with zero ratings, excluding subjects with incomplete ratings, rater mode imputation, and subject mode imputation, were compared using bias and mean squared error (MSE) as metrics.
resultsWhen disease prevalence was balanced or the missing data mechanism was missing completely at random (MCAR) or at random (MAR), excluding subjects with zero ratings showed the best performance, with bias and MSE close to zero at a missing proportion below 30%. Under skewed prevalence and missing not at random (MNAR), subject mode imputation was superior for AC
conclusionsNo universally optimal method exists for handling missing data in AC
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