Dunson D B, Weinberg C R, Baird D D, Kesner J S, Wilcox A J
Biostatistics Branch, National Institute of Environmental Health Sciences, National Insitites of Health, Research Triangle Park, NC 27709, USA.
Stat Med. 2001 Mar 30;20(6):965-78. doi: 10.1002/sim.716.
In modelling human fertility one ideally accounts for timing of intercourse relative to ovulation. Measurement error in identifying the day of ovulation can bias estimates of fecundability parameters and attenuate estimates of covariate effects. In the absence of a single perfect marker of ovulation, several error prone markers are sometimes obtained. In this paper we propose a semi-parametric mixture model that uses multiple independent markers of ovulation to account for measurement error. The model assigns each method of assessing ovulation a distinct non-parametric error distribution, and corrects bias in estimates of day-specific fecundability. We use a Monte Carlo EM algorithm for joint estimation of (i) the error distribution for the markers, (ii) the error-corrected fertility parameters, and (iii) the couple-specific random effects. We apply the methods to data from a North Carolina fertility study to assess the magnitude of error in measures of ovulation based on urinary luteinizing hormone and metabolites of ovarian hormones, and estimate the corrected day-specific probabilities of clinical pregnancy. Published in 2001 by John Wiley & Sons, Ltd.
在对人类生育能力进行建模时，理想情况下应考虑性交时间与排卵的关系。确定排卵日时的测量误差可能会使受孕能力参数的估计产生偏差，并削弱协变量效应的估计。由于缺乏单一完美的排卵标志物，有时会获得多个容易出错的标志物。在本文中，我们提出了一种半参数混合模型，该模型使用多个独立的排卵标志物来考虑测量误差。该模型为每种评估排卵的方法赋予一个独特的非参数误差分布，并校正特定日期受孕能力估计中的偏差。我们使用蒙特卡罗期望最大化（EM）算法联合估计：（i）标志物的误差分布；（ii）经误差校正的生育能力参数；（iii）夫妻特定的随机效应。我们将这些方法应用于北卡罗来纳州生育研究的数据，以评估基于尿促黄体生成素和卵巢激素代谢物的排卵测量中的误差大小，并估计校正后的特定日期临床妊娠概率。由约翰·威利父子有限公司于2001年出版。
