A Bayesian latent class model for predicting gestational age in health administrative data.
A Bayesian latent class model for predicting gestational age in health administrative data.
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DOI：
10.1002/pst.2225
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发表时间：
2022-11
影响因子：
1.5
通讯作者：
Wen, Xuerong
中科院分区：
文献类型：
--
作者：
Wang, Shuang;Puggioni, Gavino;Wen, Xuerong
由于缺乏对出生时胎龄(GAB)的准确估计，卫生管理数据在产科研究中的使用往往有限。虽然一些研究已经提出了使用索赔数据库来估计GAB的算法，但如果不能纳入GAB的独特分布形状，可能会在估计和后续建模中引入偏差。为了弥补这一差距，我们开发了一个贝叶斯潜在类模型来预测GAB。我们提出了一种混合的高斯分布，并在每一类中联合拟合一个线性模型。我们的贝叶斯方法允许通过识别潜在的子组和估计特定类别的回归系数来建模种群中的异质性。后验计算采用带Gibbs采样器结构的马尔可夫链蒙特卡罗方法。我们使用偏差信息准则和渡边-秋池信息准则来选择最优的潜在类数。该方法以罗德岛州10,043名医疗补助妇女的数据集为例进行了说明。我们发现，3级和6级混合规格最大限度地提高了预测精度。根据我们的结果，医疗补助计划的女性被分为三类，以极端早产或早产、早产或“早产”和“晚产”为特征。产科并发症似乎比其他患者水平的特征对班级成员分配的影响更大。总而言之，与传统的线性模型相比，我们的方法在预测精度方面显示出优势，这是因为我们在模拟偏斜响应和总体异质性方面具有优越的灵活性。
Health administrative data are oftentimes of limited use in obstetric research due to lacking accurate estimation of gestational age at birth (GAB). Although several studies have proposed algorithms to estimate GAB using claims database, failing to incorporate the unique distributional shape of GAB, can introduce bias in estimates and subsequent modeling. To address this gap, we develop a Bayesian Latent class model to predict GAB. We propose a mixture of Gaussian distributions and jointly fit a linear model within each class. Our Bayesian approach allows modeling heterogeneity in the population by identifying latent subgroups and estimation of class-specific regression coefficients. Posterior computation is conducted using Markov Chain Monte Carlo methods with a Gibbs sampler structure. We use the Deviance Information Criterion and the Watanabe - Akaike Information Criterion to select the optimal number of latent classes. The method is illustrated with a dataset of 10,043 Rhode Island Medicaid women. We found that the 3-class and 6-class mixture specifications maximize prediction accuracy. Based on our results, Medicaid women were partitioned into three classes, featured by extreme preterm or preterm birth, preterm or “early” term birth, and “late” term birth. Obstetrical complications appeared to pose more significant influence on class-membership allocation than other patient-level characteristics. Altogether, compared to traditional linear models our approach shows an advantage in predictive accuracy, because of superior flexibility in modeling a skewed response and population heterogeneity.
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