Guo Siyuan, Zhang Jiajia, McLain Alexander C
Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, USA.
J R Stat Soc Ser C Appl Stat. 2024 Aug 19;73(5):1355-1369. doi: 10.1093/jrsssc/qlae039. eCollection 2024 Nov.
The motivation for this paper is to determine factors associated with time-to-fertility treatment (TTFT) among women currently attempting pregnancy in a cross-sectional sample. Challenges arise due to dependence between time-to-pregnancy (TTP) and TTFT. We propose appending a marginal accelerated failure time model to identify risk factors of TTFT with a model for TTP where fertility treatment is included as a time-varying treatment to account for their dependence. The latter requires extending backwards recurrence survival methods to incorporate time-varying covariates with time-varying coefficients. Since backwards recurrence survival methods are a function of mean survival, computational difficulties arise in formulating mean survival when fertility treatment is unobserved, i.e. when TTFT is censored. We address these challenges by developing computationally friendly forms for the double expectation of TTP and TTFT. The performance is validated via comprehensive simulation studies. We apply our approach to the National Survey of Family Growth and explore factors related to prolonged TTFT in the U.S.
本文的目的是在一个横断面样本中，确定当前正在尝试怀孕的女性中与生育治疗时间（TTFT）相关的因素。由于怀孕时间（TTP）和TTFT之间的相关性，出现了一些挑战。我们建议附加一个边际加速失效时间模型，通过一个TTP模型来识别TTFT的风险因素，其中生育治疗作为一个随时间变化的治疗因素来考虑它们之间的相关性。后者需要扩展向后递推生存方法，以纳入具有随时间变化系数的随时间变化的协变量。由于向后递推生存方法是平均生存的函数，当生育治疗未被观察到时，即在TTFT被截尾时，在制定平均生存时会出现计算困难。我们通过为TTP和TTFT的双重期望开发计算友好的形式来应对这些挑战。通过全面的模拟研究验证了该方法的性能。我们将我们的方法应用于全国家庭增长调查，并探索美国与TTFT延长相关的因素。
