EXAMINING FACTORS AFFECTING DELAYED COMPLETION OF ADJUVANT CHEMO FOR PATIENTS WITH BREAST CANCER: DEVELOPMENT OF RIDGE LOGISTIC PANEL ESTIMATORS

Abstract

The problem of multicollinearity among predictor (independent) variables is a frequent issue in logistic panel data analysis. The model parameters are estimated via the conditional maximum likelihood and unconditional maximum likelihood estimators. In this context, this paper proposes a ridge regression estimation via shrinkage methods to analyze such data. Furthermore, in view of obtaining more efficient estimators, we propose ridge estimators using different shrinkage parameters for the fixed effects logistic panel data model. An application is also presented to assess the performance of the proposed ridge estimators. The most significant factors that affect delayed completion of adjuvant chemotherapy in patients with breast cancer plus their existing outcomes in order to shed light on the link between chemotherapy duration and its outcomes according to breast cancer are illustrated in the study. The study results show that the conditional fixed effects logit estimator is more efficient and better than the unconditional pooling and unconditional fixed effects logit estimators. Moreover, we find that there are very influential factors that affected delayed completion of adjuvant chemotherapy such as Body Surface Area (BSA), Hemoglobin (HGB), Alanine Transaminase (ALT) and Creatinine (SRCR).