Self-reporting and screening: Data with right-censored, left-censored, and complete observations

Area of Science:

• Biostatistics
• Survival Analysis
• Medical Data Analysis

Background:

• Survival data often includes uncensored, right-censored, and left-censored observations.
• Self-detection of medical conditions naturally leads to mixed-type survival data.
• Accurate estimation of failure-time distributions is crucial for medical research and patient management.

Purpose of the Study:

• To develop a novel methodology for estimating failure-time distributions from data combining uncensored, right-censored, and left-censored observations.
• To evaluate the performance of the proposed estimators using simulated data.
• To demonstrate the application of the methodology in a real-world scenario, such as patient waiting times in emergency departments.

Main Methods:

• Proposed a novel methodology integrating semiparametric and nonparametric techniques for distribution estimation.
• Utilized simulated data to rigorously evaluate the performance of the developed estimators.
• Applied the methodology to a case study involving emergency department patient patience times.

Main Results:

• The proposed methodology effectively estimates failure-time distributions from mixed-type survival data.
• Simulations confirmed the robust performance of the developed estimators.
• The case study successfully estimated patient patience in an emergency department setting.

Conclusions:

• The novel methodology provides a powerful tool for analyzing complex survival data with mixed censoring types.
• This approach has broad applicability in various fields, including medical screening and healthcare system analysis.
• Accurate estimation of patient waiting times can inform operational improvements in emergency departments.