Variable Selection for High-Dimensional Longitudinal Data via Within-Cluster Resampling

The phenomenon of informative cluster size (ICS) emerges when the number of repeated measurements is correlated with the outcome variable. In such scenarios, the prevailing generalized estimating equation (GEE) method often yields biased estimates due to nonignorable cluster size. This study proposes an integrated methodology that explicitly accounts for ICS and provides a robust solution to mitigate its effects. Our approach combines within-cluster resampling (WCR) with a penalized likelihood framework, ensuring consistent model selection and parameter estimation across resampled datasets. Additionally, we introduce a penalized mean regression method to aggregate the estimators from multiple resampled datasets, producing a final estimator that improves the true positive discovery rate while controlling false positives. The proposed penalized likelihood method via WCR (PLWCR) is evaluated through extensive simulations and an application to yeast cell-cycle gene expression data. The results demonstrate its robustness and superior performance in high-dimensional longitudinal data analysis with ICS.