Estimation in the single change-point hazard function for interval-censored data with a cure fraction

Area of Science:

• Statistics
• Reliability Engineering
• Survival Analysis

Background:

• The hazard function is critical in survival analysis, representing instantaneous failure rates.
• Abrupt changes in hazard functions can occur due to events like maintenance or medical interventions.
• Identifying these change points and quantifying their magnitude is essential for accurate modeling.

Purpose of the Study:

• To propose a novel single change-point model for interval-censored survival data.
• To incorporate a cure fraction into the change-point model.
• To develop and validate estimation methods for the proposed model.

Main Methods:

• Assumed a piecewise constant hazard function with a single jump at an unknown time.
• Developed estimation techniques for the single change-point model.
• Established large-sample properties of the derived estimators.

Main Results:

• The proposed estimation methods were investigated.
• Large-sample properties of the estimators were theoretically established.
• Simulation studies demonstrated the effectiveness of the estimating method.

Conclusions:

• The single change-point model effectively handles interval-censored survival data with a cure fraction.
• The developed estimation methods are statistically sound and perform well.
• The model and methods are applicable to real-world data, such as liver and breast cancer datasets.