Analyzing the Relationship Between Censoring Mechanisms and Bias in Survival Data Estimation Models

Posted: Feb 07, 2023

• Mateo Smith
• Levi Campbell
• Mateo Nelson

Abstract

Survival analysis represents a cornerstone methodology in numerous scientific disciplines, from medical research to engineering reliability studies. The fundamental challenge in survival data analysis stems from the presence of censoring, where the exact event time remains unobserved for some subjects. While traditional approaches have predominantly addressed right-censoring scenarios, the complex relationships between various censoring mechanisms and estimation biases remain inadequately characterized. This research addresses this critical gap by systematically investigating how different censoring patterns influence parameter estimation accuracy across multiple survival modeling frameworks. The conventional paradigm in survival analysis often treats censoring as a nuisance parameter rather than a systematic source of bias. This perspective has led to methodological developments that primarily focus on handling right-censoring while largely neglecting the nuanced effects of alternative censoring mechanisms. Our investigation challenges this paradigm by demonstrating that censoring mechanisms fundamentally shape the statistical properties of survival estimators in ways that cannot be adequately addressed through standard correction techniques. This study makes several original contributions to the field. First, we develop a comprehensive theoretical framework that characterizes the bias mechanisms induced by different censoring patterns. Second, we introduce a novel simulation methodology that generates survival data with controlled censoring mechanisms, enabling precise quantification of bias patterns. Third, we propose an adaptive bias-correction methodology that significantly improves estimation accuracy across diverse censoring scenarios. Finally, we provide empirical evidence