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    The Effect of Cluster Size for Model Performance in High-Dimensional Longitudinal Studies: A Simulation Study
    (2023) Şengül, Merve Türkegün; Tasdelen, Bahar; Yologlu, Saim
    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.