Estimating equations for survival analysis with pooled logistic regression

2025-04-17

AI Summary

In survival analysis, standard implementations of the paired logistic regression model—particularly when coupled with nonparametric bootstrap inference—are computationally prohibitive, often necessitating time discretization or strong parametric assumptions that compromise model flexibility and validity. This paper reformulates the model within an estimating equations framework, thereby eliminating the need for nonparametric bootstrap and freeing inference from both time discretization and parametric distributional assumptions. Within this framework, we develop an efficient iterative algorithm for parameter estimation and propose a robust empirical sandwich-type variance estimator. Monte Carlo simulations and real-data analyses demonstrate that the proposed method substantially reduces computational complexity, yields stable variance estimates with accurate nominal coverage, and maintains finite-sample statistical consistency. The core contribution is a hypothesis-free, scalable, and computationally efficient inferential paradigm for paired logistic regression in survival analysis.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Security and Privacy: Large-scale security measurements

Abstract

Pooled logistic regression models are commonly applied in survival analysis. However, the standard implementation can be computationally demanding, which is further exacerbated when using the nonparametric bootstrap for inference. To ease these computational burdens, investigators often coarsen time intervals or assume a parametric models for time. These approaches impose restrictive assumptions, which may not always have a well-motivated substantive justification. Here, the pooled logistic regression model is re-framed using estimating equations to simplify computations and allow for inference via the empirical sandwich variance estimator, thus avoiding the more computationally demanding bootstrap. The proposed method is demonstrated using two examples with publicly available data. The performance of the empirical sandwich variance estimator is illustrated using a Monte Carlo simulation study. The implementation proposed here offers an improved alternative to the standard implementation of pooled logistic regression without needing to impose restrictive constraints on time.

Problem

• Reduces computational burden in survival analysis
• Avoids restrictive time interval assumptions
• Simplifies inference using estimating equations

Innovation

• Estimating equations simplify pooled logistic regression
• Empirical sandwich variance estimator replaces bootstrap
• Avoids restrictive time constraints in survival analysis