Performance of multiple imputation methods for incomplete normally distributed longitudinal data

Tuncay Yanarateş

EN TR

Performance of multiple imputation methods for incomplete normally distributed longitudinal data

Abstract

Missing data can often occur in longitudinal data. Multiple imputation has been very popular for longitudinal analysis in recent years. In this study, we aim to determine which multiple imputation method is superior for longitudinal normally distributed data when we use linear mixed models. In the literature, multiple imputation by chained equations (MICE) is one of the most popular methods for longitudinal data when using linear mixed models. In MICE, there are three parametric multiple imputation methods, and we compare the three methods with maximum likelihood multiple imputation. Maximum likelihood multiple imputation estimates the parameters using maximum likelihood. After the literature review, we determined that this study will be the first to compare these two approaches for longitudinal normally distributed data when we use linear mixed models. After the simulation study, maximum likelihood multiple imputation has less biased results than the three methods in MICE in terms of mean square error (MSE). The three imputation methods in MICE give closer results to each other. Therefore, we find that maximum likelihood multiple imputation is superior to MICE for longitudinal normally distributed data when we use linear mixed models. Moreover, we show that maximum likelihood multiple imputation can be used for longitudinal normally distributed data with linear mixed models.

Keywords

Eksik normal dağılımlı boylamsal veriler için çoklu atama yöntemlerinin performansı

Abstract

Boylamsal verilerde sıklıkla eksik veri oluşabilir. Çoklu atama, son yıllarda boylamsal veri analizi için oldukça popüler hale gelmiştir. Bu çalışmada, doğrusal karma modeller kullanıldığında boylamsal normal dağılımlı veriler için hangi çoklu atama yönteminin daha üstün olduğu belirlenmeye çalışılmaktadır. Literatürde, doğrusal karma modeller kullanıldığında boylamsal veriler için en popüler yöntemlerden biri zincirleme denklemlerle çoklu atama (MICE) yöntemidir. MICE'de üç parametrik çoklu atama yöntemi vardır ve bu üç yöntem en çok olabilirlik çoklu atama yöntemiyle karşılaştırılmaktadır. En çok olabilirlik çoklu atama, parametreleri en çok olabilirlik kullanarak tahmin eder. Literatür taramasının ardından, bu çalışmanın doğrusal karma modeller kullanıldığında boylamsal normal dağılımlı veriler için bu iki yaklaşımın karşılaştırıldığı ilk çalışma olduğu belirlenmiştir. Simülasyon çalışmasının ardından en çok olabilirlik çoklu atama yönteminin MICE'deki üç yöntemden ortalama kare hata bakımından daha az yanlı sonuçlar verdiği görülmüştür. MICE’deki üç yöntem birbirine yakın sonuçlar vermiştir. Bu nedenle, doğrusal karma modeller kullanıldığında boylamsal normal dağılımlı veriler için en çok olabilirlik çoklu atama yöntemi MICE'den daha üstün olduğu bulunmuştur. Ayrıca, en çok olabilirlik çoklu atama yönteminin, doğrusal karma modellerle birlikte boylamsal normal dağılımlı veriler için kullanılabileceği gösterilmiştir.

Keywords

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Details

Primary Language

English

Subjects

Statistical Analysis

Journal Section

Research Article

Authors

Tuncay Yanarateş ^*
0000-0001-8654-3329
Türkiye

Publication Date

April 23, 2026

Submission Date

January 17, 2026

Acceptance Date

April 23, 2026

Published in Issue

Year 2026 Number: 2026

IZ

https://izlik.org/JA67ED77YA

Cite

RIS / Bibtex

APA

Yanarateş, T. (2026). Performance of multiple imputation methods for incomplete normally distributed longitudinal data. İstatistikçiler Dergisi:İstatistik Ve Aktüerya, 2026, 22-36. https://izlik.org/JA67ED77YA

AMA

1.Yanarateş T. Performance of multiple imputation methods for incomplete normally distributed longitudinal data. JSSA. 2026;(2026):22-36. https://izlik.org/JA67ED77YA

Chicago

Yanarateş, Tuncay. 2026. “Performance of Multiple Imputation Methods for Incomplete Normally Distributed Longitudinal Data”. İstatistikçiler Dergisi:İstatistik Ve Aktüerya, nos. 2026: 22-36. https://izlik.org/JA67ED77YA.

EndNote

Yanarateş T (April 1, 2026) Performance of multiple imputation methods for incomplete normally distributed longitudinal data. İstatistikçiler Dergisi:İstatistik ve Aktüerya 2026 22–36.

IEEE

[1]T. Yanarateş, “Performance of multiple imputation methods for incomplete normally distributed longitudinal data”, JSSA, no. 2026, pp. 22–36, Apr. 2026, [Online]. Available: https://izlik.org/JA67ED77YA

ISNAD

Yanarateş, Tuncay. “Performance of Multiple Imputation Methods for Incomplete Normally Distributed Longitudinal Data”. İstatistikçiler Dergisi:İstatistik ve Aktüerya. 2026 (April 1, 2026): 22-36. https://izlik.org/JA67ED77YA.

JAMA

1.Yanarateş T. Performance of multiple imputation methods for incomplete normally distributed longitudinal data. JSSA. 2026;:22–36.

MLA

Yanarateş, Tuncay. “Performance of Multiple Imputation Methods for Incomplete Normally Distributed Longitudinal Data”. İstatistikçiler Dergisi:İstatistik Ve Aktüerya, no. 2026, Apr. 2026, pp. 22-36, https://izlik.org/JA67ED77YA.

Vancouver

1.Tuncay Yanarateş. Performance of multiple imputation methods for incomplete normally distributed longitudinal data. JSSA [Internet]. 2026 Apr. 1;(2026):22-36. Available from: https://izlik.org/JA67ED77YA

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