The landscape of survival analysis is constantly being revolutionized to answer biomedical challenges, most recently the statistical challenge of censored covariates rather than outcomes. There are many promising strategies to tackle censored covariates, including weighting, imputation, maximum likelihood, and Bayesian methods. Still, this is a relatively fresh area of research, different from the areas of censored outcomes (i.e., survival analysis) or missing covariates. In this review, we discuss the unique statistical challenges encountered when handling censored covariates and provide an in-depth review of existing methods designed to address those challenges. We emphasize each method's relative strengths and weaknesses, providing recommendations to help investigators pinpoint the best approach to handling censored covariates in their data.

中文翻译：

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理解删失协变量：亨廷顿病研究的统计方法


生存分析的格局不断发生变革，以应对生物医学挑战，最近的挑战是审查协变量而不是结果的统计挑战。有许多有前途的策略来处理审查协变量，包括加权、插补、最大似然和贝叶斯方法。尽管如此，这是一个相对较新的研究领域，不同于审查结果（即生存分析）或缺失协变量的领域。在这篇综述中，我们讨论了处理审查协变量时遇到的独特统计挑战，并对旨在解决这些挑战的现有方法进行了深入回顾。我们强调每种方法的相对优点和缺点，提供建议来帮助研究人员确定处理数据中审查协变量的最佳方法。