This personalized approach not only enhances the efficacy of treatments but also minimizes the risk of adverse effects (Agyeman and Ofori-Asenso, 2015; Kumari et al., 2023). However, the success of precision medicine heavily relies on the interpretation of complex, multidimensional data sets, where statistical analysis plays a pivotal role (Alyass et al., 2015). The integration of statistical methodologies in precision medicine is not just a mere addition but a fundamental necessity. Advanced statistical techniques enable the extraction of meaningful insights from large-scale genomics, proteomic, and metabolomic data, which are the cornerstone of precision medicine (Wafi and Mirnezami, 2018; Pinu et al., 2019). These methodologies include, but are not limited to, predictive modeling, machine learning algorithms, and complex data visualization techniques, all of which contribute to more accurate diagnosis, prognosis, and treatment planning (Bellazzi and Zupan, 2008; Davatzikos et al., 2018; Richter and Khoshgoftaar, 2018). The heterogeneity of data sources in precision medicine, ranging from electronic health records (EHRs) to high-throughput sequencing data, presents unique challenges in data integration and interpretation (Martinez-Garcia and Hernández-Lemus, 2022). Statistical analysis serves as a bridge, merging these diverse data types into coherent, interpretable information that can guide clinical decision-making. However, the field is not without its challenges. Issues such as overfitting, handling of highdimensional data, and maintaining the balance between model complexity and interpretability are ongoing areas of research (Bolón-Canedo et al., 2015; Xu et al., 2019; Bommert, 2020; Pes, 2020; Hou and Behdinan, 2022).