Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
A Comparison of Multiple Imputation Methods in Multiple Imputation by Chained Equations for Longitudinal Continuous Data: A Simulation Study
Uzunlamasına Sürekli Veri İçin Zincirlenmiş Denklemlerle Çoklu Atama İçindeki Çoklu Atama Yöntemlerinin Karşılaştırılması: Bir Simülasyon Çalışması
Tuncay YANARATEŞa , Erdem KARABULUTa
aHacettepe University Faculty of Medicine, Department of Biostatistics, Ankara, Türkiye
Turkiye Klinikleri J Biostat. 2025;17(2):72-80
doi: 10.5336/biostatic.2025-108815
Article Language: EN
Full Text
ABSTRACT
 Objective: Missing data is one of the main problems in longitudinal data. Imputation is one of the ways to solve this problem. Multiple imputation methods are preferred to single imputation methods because they explain the uncertainty around the true value and get almost unbiased estimates. In this study, we aim to compare 5 multiple imputation methods within multiple imputation by chained equations (MICE) for longitudinal continuous data. Material and Methods: We evaluated the performance of the 5 methods by generating data from multivariate distribution in R programming language. We deleted 10%, 20%, and 30% of the complete data under missing completely at random and missing at random. We simulated 1,000 repetitions. Our evaluation criterion is root mean squared error. Results: When there is a weak correlation in time points, MICE-random forest (MICE-RF) has the least biased results. When there is a strong correlation in time points, MICE-predictive mean matching (MICE-PMM) and MICE using linear regression with bootstrap (MICE-BOOT) have the least biased results. MICE-classification and regression trees and MICE using Bayesian linear regression have the most biased results. Conclusion: MICE-RF, MICEPMM, and MICE-BOOT can be used for longitudinal continuous data with missing observations. Moreover, researchers can generate different multivariate distributions in simulation studies to determine the optimal method.

Keywords: Longitudinal data; missing data; missing completely at random; multiple imputation
ÖZET
 Amaç: Eksik veri, uzunlamasına veri analizinde temel sorunlardan biridir. Bu sorunu çözmek için bilinen yollardan biri atamadır. Çoklu atama yöntemleri, gerçek değer etrafındaki belirsizliği açıkladıkları ve neredeyse yansız tahminler elde ettikleri için tekli atama yöntemlerine tercih edilmektedir. Bu çalışmada, uzunlamasına veri için MICE içindeki 5 çoklu atama yönteminin karşılaştırılması amaçlanmaktadır. Gereç ve Yöntemler: Beş yöntemin performansı, R programlama dilinden çok değişkenli dağılım üreterek değerlendirilmiştir. Tamamen rastgele kayıp ve rastgele kayıp mekanizmaları altında tam verilerin %10, %20, %30'u silinmiştir. 1.000 tekrar yapılmıştır. Değerlendirme kriteri olarak karekök ortalama kare hata belirlenmiştir. Bulgular: Zaman noktaları arasında zayıf korelasyon varken, MICE-rastgele orman [random forest (RF)] (MICE-RF) en yansız sonuçlara sahiptir. Zaman noktaları arasında güçlü korelasyon varken, bootstrap ile doğrusal regresyon kullanan MICE [MICE using linear regression with bootstrap (MICE-BOOT)] ve MICE-tahmini ortalama eşleştirme [MICE-predictive mean matching (MICE-PMM)] en yansız sonuçlara sahiptir. MICE-sınıflandırma ve regresyon ağaçları ve Bayesian doğrusal regresyonu kullanan MICE en yanlı sonuçlara sahiptir. Sonuç: MICE-RF, MICE-PMM ve MICE-BOOT kayıp veriye sahip uzunlamasına veri için kullanılabilir. Ayrıca araştırmacılar, benzetim çalışmalarında farklı çok değişkenli dağılımlar üretebilirler.

Anahtar Kelimeler: Uzunlamasına veri; kayıp veri; tamamen rastgele kayıp; çoklu atama
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