Survival Analysis With Heterogeneous Covariate Measurement Error
协变量测量误差异质时的生存分析AI 解读 ↓
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协变量测量误差异质时的生存分析
- 研究问题
- 当生存分析中的协变量测量误差具有异质性，或协变量测量时点错位时，朴素偏似然估计和无关联检验会受到什么影响？
- 方法与思路
- 作者建立一般分析框架，研究朴素偏似然估计及得分检验的渐近行为，并据此构造简单偏差修正估计与样本量公式。
- 材料陈述的贡献
- 摘要称，在协变量与误差各分量相互独立时出现向零衰减，并给出违反独立条件时的反例；小至中等误差下修正模拟表现良好。
- 阅读关注点
- 应核对异质误差及相互独立的精确定义、反向衰减的反例，以及修正估计、局部备择检验和样本量公式的条件。
- 阅读边界
- 依据版本未注明的 OpenAlex 摘要；误差结构细节和模拟数值未说明，向零衰减结论不可脱离明确的独立条件。
原文摘要
This article is motivated by a time-to-event analysis where the covariate of interest was measured at the wrong time. We show that the problem can be formulated as a special case of survival analysis with heterogeneous covariate measurement error and develop a general analytic framework. We study the asymptotic behavior of the naive partial likelihood estimates and analytically demonstrate that under the heterogeneous measurement error structure and the assumption that all components of the covariate vector and the measurement error vector combined are mutually independent, these naive estimates will shrink toward 0, and that the degree of attenuation increases as the measurement error increases. We also give counterexamples for reverse attenuation when the independence conditions are violated. We use our analytical results to derive a simple bias-correcting estimator that performs well in simulations for small and moderate amounts of measurement error. Our framework can be used to provide insight into the behavior of the commonly used partial likelihood score test for testing no association between a failure outcome and an exposure, for example, in the presence of measurement error or mistiming error. In particular, we derive the asymptotic distribution of the naive partial likelihood score test under a series of local alternatives and discuss the asymptotic relative efficiency. As a result, a simple sample size formula to account for the contamination of covariates is obtained.