Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, 6100 Executive Boulevard, Rockville, MD 20852, USA.
Biostatistics. 2012 Jan;13(1):4-17. doi: 10.1093/biostatistics/kxr015. Epub 2011 Jun 22.
Understanding conception probabilities is important not only for helping couples to achieve pregnancy but also in identifying acute or chronic reproductive toxicants that affect the highly timed and interrelated processes underlying hormonal profiles, ovulation, libido, and conception during menstrual cycles. Currently, 2 statistical approaches are available for estimating conception probabilities depending upon the research question and extent of data collection during the menstrual cycle: a survival approach when interested in modeling time-to-pregnancy (TTP) in relation to women or couples' purported exposure(s), or a hierarchical Bayesian approach when one is interested in modeling day-specific conception probabilities during the estimated fertile window. We propose a biologically valid discrete survival model that unifies the above 2 approaches while relaxing some assumptions that may not be consistent with human reproduction or behavior. This approach combines both the survival and the hierarchical models allowing investigators to obtain the distribution of TTP and day-specific probabilities during the fertile window in a single model. Our model allows for the consideration of covariate effects at both the cycle and the daily level while accounting for daily variation in conception. We conduct extensive simulations and utilize the New York State Angler Prospective Pregnancy Cohort Study to illustrate our approach. We also provide the code to implement the model in R software in the supplemental section of the supplementary material available at Biostatistics online.
了解受孕概率不仅对帮助夫妇实现妊娠很重要，而且还可以识别出急性或慢性生殖毒物，这些毒物会影响荷尔蒙谱、排卵、性欲和月经周期中受孕的高度定时和相互关联的过程。目前，有 2 种统计方法可用于估算受孕概率，具体取决于研究问题和月经周期期间数据收集的程度：当对与妇女或夫妇的假定暴露（如果有）有关的妊娠时间（TTP）建模感兴趣时，可以使用生存方法，或者当对在估计的可育窗口期间建模特定日期的受孕概率感兴趣时，可以使用分层贝叶斯方法。我们提出了一个具有生物学意义的离散生存模型，它统一了上述 2 种方法，同时放宽了一些可能与人类生殖或行为不一致的假设。这种方法结合了生存模型和分层模型，使研究人员能够在单个模型中获得可育窗口内 TTP 和特定日期概率的分布。我们的模型允许在考虑周期和每日水平的协变量效应的同时，考虑受孕的每日变化。我们进行了广泛的模拟，并利用纽约州钓鱼者前瞻性妊娠队列研究来说明我们的方法。我们还在补充材料的补充部分中提供了在 R 软件中实现模型的代码，可在 Biostatistics 在线获取。
