Turkiye Klinikleri Journal of Biostatistics

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Tekrarlanan Ölçümlü Verilerde Kovaryans Modelleri
Covariance Models in Repeated Measured Data
Şirin ÇETİNa, Yüksel BEKb
aBiyoistatistik AD, Tokat Gaziosmanpaşa Üniversitesi Tıp Fakültesi, Tokat, TÜRKİYE
bBiyoistatistik AD, Ondokuz Mayıs Üniversitesi Tıp Fakültesi, Samsun/TÜRKİYE
Turkiye Klinikleri J Biostat. 2019;11(3):239-53
doi: 10.5336/biostatic.2019-66645
Article Language: TR
Full Text
ÖZET
 Amaç: Sağlık alanında tüm hastalardan aynı zaman noktalarında ölçüm yapmak her zaman mümkün değildir. İşlemler her bireyde eşit aralıklarla yapılsa bile farklı zamanlarda başlayabilir. Bazen de işlemin bir gereksinimi olarak değişik zaman aralıkları ile ölçümler yapılır. Bu tür tekrarlanan ölçümlü verilerin analizinden doğru sonuçlar elde edebilmek için doğru bir kovaryans modeline ihtiyaç vardır. Tekrarlanan ölçümlü veri analizinde, veriye en uygun varyans kovaryans yapısının belirlenmesi doğru çözüm için esastır. Bu çalışmanın amacı tekrarlanan ölçümlü verilerin analizinde varyans kovaryans modeli hatalı seçildiğinde oluşabilecek farklılıkları saptayarak nihai kararı ne kadar etkilebileceğini test etmektir. Gereç ve Yöntemler: Çalışmada varyans kovaryans yapısına uygun veriler SAS paket programı kodlama sistemi içerisinde simülasyon ile türetilerek gerçek varyans kovaryans yapısı bilinen bu veri setleri farklı varyans kovaryans yapıları ile analiz edilerek, hatalı varyans kovaryans yapısı seçiminin analiz üzerine etkisi incelendi. Bulgular: Çalışmada tekrarlanan ölçümlü verilerin analizi için en uygun varyans kovaryans yapısı, BIC değeri minimum olan olan Yapılanmamış (UN) varyans kovaryans modeli olarak belirlendi. Veri Yapılanmamış (UN) varyans kovaryans modeli ile analiz edildiğinde ilaç etkisi önemli olarak bulundu. GLM ile analiz edildiğinde ilaç etkisi önemsiz olarak bulundu. Sonuç: Eksik verili tekrarlanan ölçümlerin analizinde GLM çözüm tekniğinin kullanılması kararların hatalı olmasına sebebiyet verebilir. Bu sebeple eksik verili tekrarlanan ölçümlerde MIXED çözüm tekniği kullanılması önerilmektedir. Aksi halde gerçekçi olmayan kararların verilmesi söz konusudur.

Anahtar Kelimeler: GLM; MIXED; Tekrarlanan Ölçüm; Kovaryans Modelleri
ABSTRACT
 Objective: It is not always possible to make measurements at the same time points from all patients in the field of health. Transactions may begin at different times, even if they are performed at equal intervals in each individual. Occasionally, measurements are made at different time intervals as a requirement of the process. In order to obtain accurate results from the analysis of such repeated measurement data, an accurate model of covariance is needed. In repeated data analysis, determining the most appropriate variance covariance structure for data is essential for the correct solution. The aim of this study is to test the effects of repeated measurement data and determine the differences that may occur when the variance covariance model is selected incorrectly and to test the final decision. Material and Methods: In this study, the data on the variance covariance structure and SAS package program coding system were analyzed by using different variance covariance structures and the effect on the analysis of the selection of incorrect variance covariance structure was investigated. Findings: In the study, the most appropriate variance covariance structure for the analysis of repeated measured data was determined as Unstructured (UN) variance covariance model with minimum BIC value. When the data were analyzed by Univariate (UN) varicence covariance model, the drug effect was found to be significant. When analyzed by GLM, the drug effect was insignificant. Conclusion: The use of the GLM solution technique in the analysis of incomplete data may result in erroneous decisions. For this reason, it is recommended to use MIXED solution technique for repeated measurements. Otherwise, unrealistic decisions are made.

Keywords: GLM; MIXED; Repeated Measures; Covariance Structures
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