Cox Regression Model Under Dependent Truncation

TL;DR

This paper introduces an EM algorithm for Cox regression that relaxes the independence assumption between survival and truncation times, allowing for more accurate analysis under dependent truncation.

Contribution

It develops a novel EM-based Cox regression method that handles dependent truncation by assuming conditional independence, improving bias and mean-squared error over existing methods.

Findings

Estimators are consistent and asymptotically normal.

Simulations show reduced bias and lower mean-squared error.

Applied to Alzheimer's disease data to assess occupation effects.

Abstract

Truncation is a statistical phenomenon that occurs in many time to event studies. For example, autopsy-confirmed studies of neurodegenerative diseases are subject to an inherent left and right truncation, also known as double truncation. When the goal is to study the effect of risk factors on survival, the standard Cox regression model cannot be used when the data is subject to truncation. Existing methods which adjust for both left and right truncation in the Cox regression model require independence between the survival times and truncation times, which may not be a reasonable assumption in practice. We propose an expectation-maximization algorithm to relax the independence assumption in the Cox regression model under left, right, or double truncation, to an assumption of conditional independence. The resulting regression coefficient estimators are consistent and asymptotically normal. We demonstrate through extensive simulations that the proposed estimators have little bias and, in most practical situations, have a lower mean-squared error compared to existing estimators. We implement our approach to assess the effect of occupation on survival in subjects with autopsy-confirmed Alzheimer's disease.