There are controversial studies investigating whether multiple anti-Mullerian hormone (AMH) measurements can improve the individualized prediction of age at menopause in the general population. This study aimed to reexplore the additive role of the AMH decline rate in single AMH measurement for improving the prediction of age at physiological menopause, based on two common statistical models for analysis of time-to-event data, including time-dependent Cox regression and Cox proportional-hazards regression models.

A total of 901 eligible women, aged 18–50 years, were recruited from the Tehran Lipid and Glucose Study (TLGS) population and followed up every 3 years for 18 years. The serum AMH level was measured at the time of recruitment and twice after recruitment within 6-year intervals using the Gen II AMH assay. The added value of repeated AMH measurements for the prediction of age at menopause was explored using two different statistical approaches. In the first approach, a time-dependent Cox model was plotted, with all three AMH measurements as time-varying predictors and the baseline age and logarithm of annual AMH decline as time-invariant predictors. In the second approach, a Cox proportional-hazards model was fitted to the baseline data, and improvement of the complex model, which included repeated AMH measurements and the logarithm of the AMH annual decline rate, was assessed using the C-statistic.

The time-dependent Cox model showed that each unit increase in the AMH level could reduce the risk of menopause by 87%. The Cox proportional-hazards model also improved the prediction of age at menopause by 3%, according to the C-statistic. The subgroup analysis for the prediction of early menopause revealed that the risk of early menopause increased by 10.8 with each unit increase in the AMH annual decline rate.

This study confirmed that multiple AMH measurements could improve the individual predictions of the risk of at physiological menopause compared to single AMH measurements. Different alternative statistical approaches can also offer the same interpretations if the essential assumptions are met.

有争议的研究调查了多重抗苗勒管激素 (AMH) 测量是否可以改善对一般人群绝经年龄的个体化预测。本研究旨在重新探索 AMH 下降率在单次 AMH 测量中的附加作用，以改进对生理绝经年龄的预测，基于用于分析事件时间数据的两种常见统计模型，包括时间依赖性 Cox 回归和Cox 比例风险回归模型。

从德黑兰血脂和葡萄糖研究 (TLGS) 人群中招募了 901 名年龄在 18-50 岁的符合条件的女性，每 3 年随访一次，为期 18 年。使用 Gen II AMH 测定法在招募时测量血清 AMH 水平，并在 6 年间隔内测量两次招募后的血清 AMH 水平。使用两种不同的统计方法探讨了重复 AMH 测量对预测绝经年龄的附加值。在第一种方法中，绘制了一个时间相关的 Cox 模型，所有三个 AMH 测量值都作为时变预测因子，基线年龄和年度 AMH 下降的对数作为时不变预测因子。在第二种方法中，将 Cox 比例风险模型拟合到基线数据，并改进复杂模型，

时间依赖性 Cox 模型显示，AMH 水平每增加一个单位，就可以将绝经风险降低 87%。根据 C 统计量，Cox 比例风险模型还将对绝经年龄的预测提高了 3%。预测提前绝经的亚组分析显示，随着 AMH 年下降率每增加一个单位，提前绝经的风险增加 10.8。

这项研究证实，与单次 AMH 测量相比，多次 AMH 测量可以改善对生理绝经风险的个体预测。如果满足基本假设，不同的替代统计方法也可以提供相同的解释。