Simulation Study: Introduction of Imputation Methods for Missing Data in Longitudinal Analysis
نویسنده
چکیده
Missing data are vital subject to perform a proper longitudinal analysis. Some just ignore and discard all missing data to have complete dataset. However, it can result in a very substantial loss of information. Therefore, it is important to comprehend imputation methods of handling missing data. This paper discusses four common imputation methods for longitudinal analysis. Then, using simulation study, comparison and accuracy of these imputation methods are illustrated. The final section provides summary. Mathematical Subject Classification: 62-07, 62H15, 62Q05
منابع مشابه
چند رویکرد برخورد با مقادیر گمشده متغیرهای کمی و بررسی اثر آنها بر نتایج حاصل از یک کارآزمایی بالینی
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