McLain Alexander C, Guo Siyuan, Zhang Jiajia, Marie Thoma
Department of Epidemiology and Biostatistics, University of South Carolina.
Department of Family Health Services, University of Maryland.
Ann Appl Stat. 2021 Jun;15(2):1054-1067. doi: 10.1214/20-AOAS1428. Epub 2021 Jul 12.
Cross-sectional length-biased data arise from questions on the at-risk time for an event of interest from those who are at-risk but have yet to experience the event. For example, in the National Survey on Family Growth (NSFG), women who were currently attempting to become pregnant were asked how long they had been attempting pregnancy. Cross-sectional survival analysis methods use the observed at-risk times to make inference on the distribution of the unobserved time-to-failure. However, methodological gaps in these methods remain such as how to handle semi-competing risks. For example, if the women attempting pregnancy had undergone fertility treatment during their current pregnancy attempt. In this paper, we develop statistical methods that extend cross-sectional survival analysis methods to incorporate semi-competing risks. They can be used to estimate the distribution of the length of natural pregnancy attempts (i.e., without fertility treatment) while correctly accounting for women that sought fertility treatment prior to being sampled using cross-sectional data. We demonstrate our approach based on simulated data and an analysis of data from the NSFG. The proposed method results in separate survival curves for: time-to-natural-pregnancy, time-to-fertility treatment, and time-to-pregnancy after fertility treatment.
横断面长度偏倚数据源于对感兴趣事件的风险时间的询问，这些问题针对的是处于风险中但尚未经历该事件的人群。例如，在全国生育情况调查（NSFG）中，询问了当时正在尝试怀孕的女性她们尝试怀孕的时间有多长。横断面生存分析方法利用观察到的风险时间来推断未观察到的失效时间的分布。然而，这些方法仍存在方法学上的差距，比如如何处理半竞争风险。例如，如果尝试怀孕的女性在当前的怀孕尝试期间接受了生育治疗。在本文中，我们开发了统计方法，扩展了横断面生存分析方法以纳入半竞争风险。它们可用于估计自然怀孕尝试（即未接受生育治疗）的时长分布，同时正确考虑在使用横断面数据进行抽样之前寻求生育治疗的女性。我们基于模拟数据和对NSFG数据的分析展示了我们的方法。所提出的方法为以下情况得出了单独的生存曲线：自然怀孕时间、接受生育治疗时间以及接受生育治疗后的怀孕时间。
