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Estimation in the Cox proportional hazards model with missing not at random failure indicators

delete2026-03-27
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PRE
AI
Y
Yi Liu *
L
Liu, Kaiyuan
DOI：10.1007/s11222-026-10857-1delete
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Abstract

Abstract

En 中文
The Cox proportional hazards model is a widely used tool for analyzing survival data and evaluating covariate effects. However, standard estimation procedures typically rely on the assumption that failure indicators are fully observed or missing at random. Once the assumptions are violated, traditional estimators will become inconsistent and cause biased results. In this paper, we develop two imputation-based estimating equations for the Cox model when failure indicators are missing not at random. The asymptotic normality properties of the proposed estimators are established under some regularity conditions. Simulation studies and real data analyses demonstrate the robustness and efficiency of the proposed methods across various scenarios.
Keywords:
Missing not at random
Imputation
Cox regression model
Failure indicator

Journal

S
STATISTICS AND COMPUTING
IF:
1.6
Papers:
175
Citations:
0

Organization

O
ocean university of china
Scholars:
3.0W
Papers: 1.9W
Citations: 21
C
chinese academy of sciences
Scholars:
54.9W
Papers: 44.5W
Citations: 703
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