Log-logistic Cox Model for Breast Cancer Partly Interval Censored Data

Abstract

The research in this study is concerned with implementing techniques in data which include censored observations for the evaluation of survival analysis. Analysis of survival research has numerous distinctions in the areas of health, architecture, finance, science, and other fields and it is recognized as failure time analysis. Partly Interval Censoring (PIC) is one of the censoring strategies used in the survival analysis, which may help with several forms of data, especially the incomplete ones. Log-logistic distribution is perhaps the most widely employed lifetime delivery in durability applications. We use the log-logistic Cox model in this thesis focused on adjusted medical with PIC data, as well as simulation data based on PIC. We find that our model is effective and flexible for breast cancer PIC data and simulated data. From the analysis of our real medical data and simulation data for this specific case, we may infer that our suggested distribution better represents the complexity of the model in terms of the importance of predictions of the scale and the shape parameters. Survival distribution feature plots against failure periods are used to analyze the predicted trends of survival for the two kinds of failures