Despite its drawbacks, the complete case analysis is commonly used in regression models with missing covariates. Understanding when implementing complete cases will lead to consistent parameter estimation is vital before use. Here, our aim is to demonstrate when a complete case analysis is appropriate for a nuanced type of missing covariate, the randomly right-censored covariate. Across the censored covariate literature, different assumptions are made to ensure a complete case analysis produces a consistent estimator, which leads to confusion in practice. We make several contributions to dispel this confusion. First, we summarize the language surrounding the assumptions that lead to a consistent complete case estimator. Then, we show a unidirectional hierarchical relationship between these assumptions, which leads us to one sufficient assumption to consider before using a complete case analysis. Lastly, we conduct a simulation study to illustrate the performance of a complete case analysis with a right-censored covariate under different censoring mechanism assumptions, and we demonstrate its use with a Huntington disease data example.
翻译：摘要：尽管存在缺点，但完全样本分析通常用于存在缺失协变量的回归模型。在使用之前了解何时实施完全样本将导致一致的参数估计是至关重要的。本文的目的是演示何时对于一种复杂的缺失协变量类型——随机右截尾协变量，执行完全样本分析是合适的。在截尾协变量文献中，为确保完全样本分析产生一致估计值，做出了不同的假设，这导致了实践中的混乱。我们做出了几项贡献以消除这种混乱。首先，我们总结了导致一致完全样本估计值的假设背后的语言。然后，我们展示了这些假设之间的单向层次关系，这导致了我们需要考虑一个充分的假设，然后再使用完全样本分析。最后，我们进行了一个模拟研究，以说明在不同的截尾机制假设下，使用右截尾协变量的完全样本分析的表现，并演示了在亨廷顿病数据示例中如何使用它。
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