Şengül, Merve TürkegünTasdelen, BaharYologlu, Saim2026-01-242026-01-2420231308-78942146-8877https://search.trdizin.gov.tr/tr/yayin/detay/1258730https://doi.org/10.5336/biostatic.2023-98699https://hdl.handle.net/20.500.12868/4424Objective: 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.eninfo:eu-repo/semantics/openAccessModel selectionGeneralized estimating equationspenalized generalized estimating equationspenalized methodshigh dimensional longitudinal dataArticle10.5336/biostatic.2023-986991531611701258730