BACKGROUND
Discrete-time event history analysis (EHA) is the standard approach taken when modelling fertility histories collected in surveys, where the date of birth is often recorded imprecisely. This method is commonly used to investigate the factors associated with the time to a first or subsequent conception or birth. Although there is an emerging trend towards the smooth incorporation of continuous covariates in the broader literature, this is yet to be formally embraced in the context of birth events.

OBJECTIVE
We investigate the formal application of smooth methods implemented via generalized additive models (GAMs) to the analysis of fertility histories. We also determine whether and where GAMs offer a practical improvement over existing approaches.

METHODS
We fit parity-specific logistic GAMs to data from the UK Household Longitudinal Study, learning about the effects of age, period, time since last birth, educational qualification, and country of birth. First, we select the most parsimonious GAMs that fit the data sufficiently well. Then we compare them with corresponding models that use the existing methods of categorical, polynomial, and piecewise linear spline representations in terms of fit, complexity, and substantive insights gained.

RESULTS
We find that smooth terms can offer considerable improvements in precision and efficiency, particularly for highly non-linear effects and interactions between continuous variables. Their flexibility enables the detection of important features that are missed or estimated imprecisely by comparator methods.



中文翻译：

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研究广义加性模型在出生事件的离散时间事件历史分析中的应用（作者：Joanne Ellison、Ann Berrington、Erengul Dodd、Jonathan J. Forster）

背景
离散时间事件历史分析 (EHA) 是在对调查中收集的生育历史进行建模时采用的标准方法，其中出生日期通常不精确地记录。这种方法通常用于调查与第一次或随后的受孕或出生时间相关的因素。尽管在更广泛的文献中出现了将连续协变量顺利纳入的新兴趋势，但这尚未在出生事件的背景下正式接受。

目的
我们研究通过广义加性模型 (GAM) 实施的平滑方法在生育史分析中的正式应用。我们还确定 GAM 是否以及在何处提供对现有方法的实际改进。

方法
我们对来自英国家庭纵向研究的数据进行了针对特定胎次的逻辑 GAM 的拟合，了解了年龄、时期、自上次出生以来的时间、教育资格和出生国家/地区的影响。首先，我们选择最简洁的 GAM 来充分拟合数据。然后，我们将它们与使用现有分类、多项式和分段线性样条表示方法的相应模型在拟合、复杂性和获得的实质性见解方面进行比较。

结果
我们发现平滑项可以显着提高精度和效率，特别是对于高度非线性效应和连续变量之间的相互作用。它们的灵活性能够检测比较器方法遗漏或估计不准确的重要特征。