PENDEKATAN MAZIMUM PENALIZED LIKELIHOOD UNTUK MENGESTIMASI FUNGSI BASELINE HAZARD PADA MODEL COX: STUDI KASUS PASIEN KANKER PAYUDARA

Abstract

Survival analysis is a statistical method that focuses on time-to-event variables, where the event time represents the duration a patient survives during the observation period. This study applies survival analysis to examine factors influencing the survival of breast cancer patients who are receiving treatment at C-Tech Labs Edwar Technology. The data used are right-censored survival data, referring to patients who either survived until the end of the observation period or died from unrelated causes. Risk factors analyzed include age, gender, and cancer stage, while treatment factors consist of surgery, chemotherapy, radiotherapy, and Frequency of Electro Capacitive Cancer Therapy (ECCT) usage. The Cox Proportional Hazard (PH) model combined with the Maximum Penalized Likelihood (MPL) method is used to analyze the effect of these factors on mortality risk, as well as to estimate regression coefficients and the baseline hazard function more accurately. The results indicate that age, frequency of ECCT use, and the status of additional therapies significantly affect patient survival. Older age increases the risk of death, while a higher frequency of ECCT use and the use of additional therapies help reduce that risk. Routine use of ECCT is shown to contribute to extending the survival time of breast cancer patients at C-Tech Labs Edwar Technology, Tangerang. However, potential confounding variables not examined in this study should be considered when interpreting the findings.