Ashner Marissa C, Garcia Tanya P
Department of Biostatistics and Bioinformatics, Duke University.
Department of Biostatistics, University of North Carolina at Chapel Hill.
Am Stat. 2024;78(3):335-344. doi: 10.1080/00031305.2023.2282629. Epub 2023 Dec 21.
Despite its drawbacks, the complete case analysis is commonly used in regression models with incomplete covariates. Understanding when the complete case analysis will lead to consistent parameter estimation is vital before use. Our aim here is to demonstrate when a complete case analysis is consistent for randomly right-censored covariates and to discuss the implications of its use even when consistent. 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.
尽管存在缺陷，但完全病例分析在协变量不完整的回归模型中仍被广泛使用。在使用之前，了解完全病例分析何时会导致一致的参数估计至关重要。我们在此的目的是证明完全病例分析在随机右删失协变量情况下何时是一致的，并讨论即使在一致的情况下使用它的影响。在整个删失协变量文献中，为确保完全病例分析产生一致的估计量做出了不同的假设，这在实践中导致了混淆。我们做出了几项贡献来消除这种混淆。首先，我们总结了围绕导致一致的完全病例估计量的假设的相关表述。然后，我们展示了这些假设之间的单向层次关系，这使我们在使用完全病例分析之前考虑一个充分的假设。最后，我们进行了一项模拟研究，以说明在不同删失机制假设下右删失协变量的完全病例分析的性能，并通过亨廷顿病数据示例展示其应用。
