Lotspeich Sarah C, Ashner Marissa C, Vazquez Jesus E, Richardson Brian D, Grosser Kyle F, Bodek Benjamin E, Garcia Tanya P
Department of Statistical Sciences, Wake Forest University, Winston-Salem, North Carolina, USA.
Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Annu Rev Stat Appl. 2024 Apr;11:255-277. doi: 10.1146/annurev-statistics-040522-095944. Epub 2023 Sep 8.
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.
生存分析领域正在不断变革，以应对生物医学挑战，最近面临的统计挑战是截尾协变量而非结局。有许多应对截尾协变量的有前景的策略，包括加权、插补、最大似然法和贝叶斯方法。不过，这是一个相对较新的研究领域，不同于截尾结局（即生存分析）或缺失协变量的领域。在本综述中，我们讨论处理截尾协变量时遇到的独特统计挑战，并对旨在应对这些挑战的现有方法进行深入综述。我们强调每种方法的相对优缺点，提供建议以帮助研究人员确定处理其数据中截尾协变量的最佳方法。
