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：尽管存在缺陷，完整案例分析仍常用于存在缺失协变量的回归模型中。在使用前，理解何时实施完整案例能产生一致的参数估计至关重要。本文旨在论证对于一类细微的缺失协变量——随机右删失协变量——完整案例分析何时适用。在删失协变量相关文献中，为确保完整案例分析能生成一致估计量，研究者采用了不同假设，这在实际应用中造成了混淆。为澄清此混淆，我们做出以下贡献：首先，我们梳理了与确保完整案例估计量一致性相关的假设术语；其次，我们揭示了这些假设之间的单向层级关系，并因此提出一个在使用完整案例分析前需考虑的必要充分条件；最后，通过模拟研究，我们展示了在不同删失机制假设下，含右删失协变量的完整案例分析的表现，并利用亨廷顿病数据实例加以实证。