Multiple Period Logit Model Using the Maximum Likelihood and Bayesian Approach on Data of Breast Cancer Patients in C-Tech Laboratories Tangerang

Survival statistical analysis is a method that describes the analysis of data in the form of time, starting from the time of origin until the occurrence of a special event. In certain cases, an object has a state that can change over time. Survival analysis that can detect changes in time is multiple period logit analysis. To form an analytical model, it is necessary to have an estimation. The estimations that will be used in this research are Maximum Likelihood (ML) estimation and Bayesian estimation with a prior uniform. In 2020 there were 213,546 cancer cases in Indonesia, breast cancer cases increased to 16.6% with 9.6% mortality. Currently, there is an alternative tool that is considered capable of reducing the mortality rate of breast cancer patients, namely Electro Capacitive Cancer Therapy (ECCT). ECCT, a therapeutic device in the form of a vest in which there is an alternating current electric field with low intensity (<30Vpp) and low frequency (<100KHz) to inhibit the growth of cancer cells. As a result, the factors (variabels) that potentially affect the survival of breast cancer patients following ECCT therapy at at C-Tech Lab Edwar Tangerang will be investigated in this study uses multiple period logit ML and Bayesian estimation models and identifies the performance of multiple period logit ML and multiple period logit models using Bayesian classification. From the results of the analysis of the application of the method, in this case, breast cancer patients with metastases, clinical conditions, side effects, and hours of use of the ECCT device are factors that influence the mortality of breast cancer patients undergoing ECCT therapy. at C-Tech Lab Edwar Tangerang in 2013-2017. Furthermore, the best performance of the two estimations is the ML estimation with an accuracy value of 88.37%.