Predicting birth weight with conditionally linear transformation models
Predicting birth weight with conditionally linear transformation models
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DOI：
10.1177/0962280214532745
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发表时间：
2016-12-01
影响因子：
2.3
通讯作者：
Hothorn, Torsten
中科院分区：
文献类型：
--
作者：
Moest, Lisa;Schmid, Matthias;Hothorn, Torsten
低出生体重和高出生体重是新生儿发病率和死亡率的重要危险因素。因此，妇科医生必须在分娩前准确预测BW。大多数体重预测公式是基于出生前一周内进行的产前超声测量。虽然在临床实践中成功使用，但这些公式侧重于BW的点预测，但没有系统地量化预测的不确定性，即它们导致BW的条件平均值的估计，但不提供预测区间。为了克服这个问题，我们引入条件线性变换模型（CLTM）来预测BW。CLTM不是仅关注条件均值，而是对给定产前超声参数的BW的整个条件分布函数进行建模。因此，CLTM方法提供BW的点预测和胎儿特异性预测区间。预测区间构成了一个易于解释的预测准确性的措施，并允许识别胎儿的预测不确定性高。使用的数据集8712分娩在围产期中心在大学诊所埃尔兰根（德国），我们分析了CLTM的变体，并将它们与标准的线性回归估计技术在过去使用的分位数回归方法。在条件覆盖率和预测区间的平均长度方面，表现最好的CLTM变体与分位数回归和线性回归方法具有竞争力。我们建议使用CLTM，因为它们能够解释可能的异方差，峰度和偏度分布的BW。
Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs.