McLain Alexander C, Sundaram Rajeshwari, Buck Louis Germaine M
Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, USA
Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, USA.
Stat Methods Med Res. 2016 Feb;25(1):22-36. doi: 10.1177/0962280212438646. Epub 2012 Feb 28.
The analysis of fecundity data is challenging and requires consideration of both highly timed and interrelated biologic processes in the context of essential behaviors such as sexual intercourse during the fertile window. Understanding human fecundity is further complicated by presence of a sterile population, i.e. couples unable to achieve pregnancy. Modeling techniques conducted to date have largely relied upon discrete time-to-pregnancy survival or day-specific probability models to estimate the determinants of time-to-pregnancy or acute effects, respectively. We developed a class of semi-parametric grouped transformation cure models that capture day-level variates purported to affect the cycle-level hazards of conception and, possibly, sterility. Our model's performance is assessed using simulation and longitudinal data from one of the few prospective cohort studies with preconception enrollment of women followed for 12 menstrual cycles at risk for pregnancy.
生育力数据的分析具有挑战性，需要在诸如排卵期性交等基本行为的背景下，考虑高度定时且相互关联的生物过程。不育人群（即无法受孕的夫妇）的存在使对人类生育力的理解更加复杂。迄今为止所采用的建模技术主要依靠离散的怀孕时间生存模型或特定日期概率模型，分别来估计怀孕时间的决定因素或急性效应。我们开发了一类半参数分组变换治愈模型，该模型能够捕捉那些据称会影响受孕周期水平风险以及可能影响不育的日水平变量。我们使用模拟以及来自少数前瞻性队列研究之一的纵向数据来评估模型的性能，该研究在怀孕风险期对女性进行孕前登记，并跟踪其12个月经周期。
