Huang Xiaobi, Elliott Michael R, Harlow Siobán D
Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109.
Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109 ; Survey Methodology Program, Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, MI 48106.
J R Stat Soc Ser C Appl Stat. 2014 Apr 1;63(3):445-466. doi: 10.1111/rssc.12044.
As women approach menopause, the patterns of their menstrual cycle lengths change. To study these changes, we need to jointly model both the mean and variability of cycle length. Our proposed model incorporates separate mean and variance change points for each woman and a hierarchical model to link them together, along with regression components to include predictors of menopausal onset such as age at menarche and parity. Additional complexity arises from the fact that the calendar data have substantial missingness due to hormone use, surgery, and failure to report. We integrate multiple imputation and time-to event modeling in a Bayesian estimation framework to deal with different forms of the missingness. Posterior predictive model checks are applied to evaluate the model fit. Our method successfully models patterns of women's menstrual cycle trajectories throughout their late reproductive life and identifies change points for mean and variability of segment length, providing insight into the menopausal process. More generally, our model points the way toward increasing use of joint mean-variance models to predict health outcomes and better understand disease processes.
随着女性接近更年期，她们月经周期长度的模式会发生变化。为了研究这些变化，我们需要对周期长度的均值和变异性进行联合建模。我们提出的模型为每位女性纳入了单独的均值和方差变化点，以及一个将它们联系在一起的层次模型，同时还包括回归成分，以纳入初潮年龄和产次等绝经起始预测因素。由于激素使用、手术以及未报告等原因，日历数据存在大量缺失，这又带来了额外的复杂性。我们在贝叶斯估计框架中整合多重填补和事件发生时间建模，以处理不同形式的缺失情况。应用后验预测模型检验来评估模型拟合情况。我们的方法成功地对女性整个晚育期的月经周期轨迹模式进行了建模，并确定了各段长度均值和变异性的变化点，为绝经过程提供了见解。更广泛地说，我们的模型为增加使用联合均值 - 方差模型来预测健康结果和更好地理解疾病过程指明了方向。
