Variance estimation of a survival function for interval-censored survival data

Area of Science:

• Biostatistics
• Survival Analysis
• Medical Statistics

Background:

• Interval-censored survival data are common in medical studies, particularly clinical trials.
• While survival function estimation is well-studied, variance estimation for these functions is less explored.
• Existing methods like the Greenwood formula are primarily for right-censored data.

Purpose of the Study:

• To propose a generalized Greenwood formula for variance estimation with interval-censored data.
• To present a bootstrap approach as an alternative method.
• To evaluate and compare these methods for accuracy and reliability.

Main Methods:

• Generalization of the Greenwood formula for interval-censored data.
• Implementation of a bootstrap approach for variance estimation.
• Comparative analysis using simulation studies and a real-world medical dataset.

Main Results:

• The proposed generalized Greenwood formula effectively estimates survival function variance for interval-censored data.
• The bootstrap method also provides reliable variance estimates.
• Both methods demonstrated good performance in simulation studies.

Conclusions:

• The developed methods offer robust solutions for variance estimation in interval-censored survival data.
• These approaches are valuable tools for medical researchers and biostatisticians analyzing clinical trial data.
• The findings contribute to more accurate statistical inference in survival analysis.