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Estimating Recurrence and Incidence of Preterm Birth Subject to Measurement Error in Gestational Age: A Hidden Markov Modeling Approach

机译：估计早孕的复发和发病率在妊娠期存在测量误差：一种隐马尔可夫建模方法

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摘要

Prediction of preterm birth as well as characterizing the etiological factors affecting both the recurrence and incidence of pre-term birth (defined as gestational age at birth ≤ 37 weeks) are important problems in obstetrics. The NICHD consecutive pregnancy study recently examined this question by collecting data on a cohort of women with at least two pregnancies over a fixed time interval. Unfortunately, measurement error due to the dating of conception may induce sizable error in computing gestational age at birth. This article proposes a flexible approach that accounts for measurement error in gestational age when making inference. The proposed approach is a hidden Markov model that accounts for measurement error in gestational age by exploiting the relationship between gestational age at birth and birth weight. We initially model the measurement error as being normally distributed, followed by a mixture of normals that has been proposed based on biological considerations. We examine the asymptotic bias of the proposed approach when measurement error is ignored and also compare the efficiency of this approach to a simpler hidden Markov model formulation where only gestational age and not birth weight is incorporated. The proposed model is compared with alternative models for estimating important covariate effects on the risk of subsequent preterm birth using a unique set of data from the NICHD consecutive pregnancy study.
机译：早产的预测以及表征影响早产复发和发病率（定义为出生时胎龄≤37周）的病因因素是产科的重要问题。 NICHD连续怀孕研究最近通过收集有关在固定时间间隔内至少两次怀孕的一组女性的数据来​​研究此问题。不幸的是，由于受孕日期引起的测量误差可能会在计算出生时的胎龄时引起相当大的误差。本文提出了一种灵活的方法，该方法可以在进行推断时考虑胎龄的测量误差。所提出的方法是一个隐藏的马尔可夫模型，该模型通过利用出生时的胎龄与出生体重之间的关系来解决胎龄的测量误差。我们最初将测量误差建模为正态分布，然后根据生物学考虑提出了正态混合。当忽略测量误差时，我们研究了该方法的渐近偏差，并且还将这种方法的效率与仅包含胎龄而不是出生体重的更简单的隐马尔可夫模型公式进行了比较。使用来自NICHD连续妊娠研究的唯一数据集，将提议的模型与替代模型进行比较，以评估对后续早产风险的重要协变量影响。

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