The Effect of Cluster Size for Model Performance in High-Dimensional Longitudinal Studies: A Simulation Study
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Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Objective: In order to prevent model estimation er- rors and deviations in high-dimensional longitudinal studies, risk models are established through penalized methods. The aim of this study is to examine the effect of small cluster effects on the gener- alized estimating equations (GEE) and penalized GEE (PGEE) model performances in high-dimensional longitudinal data. Mate- rial and Methods: A simulation study was designed to compare the GEE and PGEE model performances, Type I error rates, and power in two-period longitudinal data structures with different clus- ter sizes (n=20, 30, 50, 100, 200), different numbers of predictors (p=10, 20, 50) and different correlation levels between predictors (r=0.20, 0.50, 0.80). Results: It was observed that the GEE coef- ficient estimates were misleading and inconsistent, the Type I error rates were high, and the power of the test was weak at insuf- ficient cluster sizes and high correlations between predictors. Even when the number of predictors and cluster size were in the balance (p=10, n=100, 200), Type I error rates were obtanied high for GEE. Increasing the cluster size was not enough to re- duce the Type I error rate of GEE. The PGEE produced more successful results than GEE in all conditions. The power of PGEE increased to over 80% in all scenarios. Conclusion: The PGEE yielded more consistent results by controlling the relationships both within the cluster and between the predictors. In high- dimensional longitudinal studies, it was observed that the use of PGEE is more effective than GEE.
Açıklama
Anahtar Kelimeler
Model selection, Generalized estimating equations, penalized generalized estimating equations, penalized methods, high dimensional longitudinal data
Kaynak
Türkiye Klinikleri Biyoistatistik Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
15
Sayı
3
