Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02215, USA.
Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
BMC Med Res Methodol. 2021 Feb 5;21(1):24. doi: 10.1186/s12874-021-01210-3.
Early pregnancy weights are needed to quantify gestational weight gain accurately. Different methods have been used in previous studies to impute early-pregnancy weights. However, no studies have systematically compared imputed weight accuracy across different imputation techniques. This study aimed to compare four methodological approaches to imputing early-pregnancy weight, using repeated measures of pregnancy weights collected from two pregnancy cohorts in Tanzania.
The mean gestational ages at enrollment were 17.8 weeks for Study I and 10.0 weeks for Study II. Given the gestational age distributions at enrollment, early-pregnancy weights were extrapolated for Study I and interpolated for Study II. The four imputation approaches included: (i) simple imputation based on the nearest measure, (ii) simple arithmetic imputation based on the nearest two measures, (iii) mixed-effects models, and (iv) marginal models with generalized estimating equations. For the mixed-effects model and the marginal model with generalized estimating equation methods, imputation accuracy was further compared across varying degrees of model flexibility by fitting splines and polynomial terms. Additional analyses included dropping third-trimester weights, adding covariate to the models, and log-transforming weight before imputation. Mean absolute error was used to quantify imputation accuracy.
Study I included 1472 women with 6272 weight measures; Study II included 2131 individuals with 11,775 weight measures. Among the four imputation approaches, mixed-effects models had the highest accuracy (smallest mean absolute error: 1.99 kg and 1.60 kg for Studies I and II, respectively), while the other three approaches showed similar degrees of accuracy. Depending on the underlying data structure, allowing appropriate degree of model flexibility and dropping remote pregnancy weight measures may further improve the imputation performance.
Mixed-effects models had superior performance in imputing early-pregnancy weight compared to other commonly used strategies.
为了准确量化妊娠期体重增加，需要获得早期妊娠体重。既往研究中采用了不同方法来推断早期妊娠体重。然而，尚无研究系统比较过不同推断技术的推断体重准确性。本研究旨在比较四种方法推断早期妊娠体重的准确性，这些方法使用了来自坦桑尼亚两个妊娠队列的妊娠体重重复测量值。
研究 I 的平均妊娠年龄为 17.8 周，研究 II 的平均妊娠年龄为 10.0 周。鉴于入组时的妊娠年龄分布，研究 I 中推断了早期妊娠体重，研究 II 中则进行了内插推断。四种推断方法包括：（i）基于最近一次测量的简单推断，（ii）基于最近两次测量的简单算术推断，（iii）混合效应模型，以及（iv）带有广义估计方程的边缘模型。对于混合效应模型和带有广义估计方程的边缘模型方法，通过拟合样条和多项式项，进一步比较了不同程度的模型灵活性下的推断准确性。其他分析包括删除第三孕期体重、向模型中添加协变量，以及在推断前对体重进行对数转换。平均绝对误差用于量化推断准确性。
研究 I 纳入了 1472 名女性，共有 6272 次体重测量值；研究 II 纳入了 2131 名个体，共有 11775 次体重测量值。在这四种推断方法中，混合效应模型的准确性最高（对研究 I 和 II，其平均绝对误差分别为 1.99 千克和 1.60 千克），而其他三种方法的准确性相当。根据基础数据结构，允许适当程度的模型灵活性并删除远程妊娠体重测量值可能进一步提高推断性能。
与其他常用策略相比，混合效应模型在推断早期妊娠体重方面具有优越的性能。
