Centre for Research on Inner City Health, The Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond St., Toronto, Ontario, Canada.
Ann Epidemiol. 2012 Mar;22(3):151-9. doi: 10.1016/j.annepidem.2012.01.006.
Misclassification of gestational age based on the last menstrual period (LMP) in routinely collected data creates bias in newborn birthweight and gestational age-related indicators. Common correction methods have not been evaluated. We developed a normal mixture model for use with SAS software to correct misclassification of gestational age and compare its performance with other available correction methods and estimates of gestational age.
Using the 2007 United States natality file from the National Center for Health Statistics, we compared LMP preterm and postterm birth rates and gestational age-specific birthweight percentiles against a reference subset of births, where the likelihood of misclassification in gestational age was minimized, before and after correction by a normal mixture model, two truncation methods, and the clinical/obstetric estimate of gestational age.
The mixture model corrected preterm and postterm birth rates by 90% and 41% respectively, but previous methods performed poorly. The mixture model was also superior in correcting birthweight percentiles 50 and 90 with error reductions in the range of 68% to 85% between 28 and 36 weeks of gestation, where most misclassification occurred.
The mixture model behaved consistently better than truncation methods, particularly between weeks 28 and 36 of gestation.
基于末次月经（LMP）的常规收集数据对胎龄进行分类错误，会导致新生儿出生体重和与胎龄相关的指标产生偏差。目前尚未评估常见的校正方法。我们开发了一种适用于 SAS 软件的正态混合模型，用于校正胎龄分类错误，并将其与其他可用的校正方法和胎龄估计值进行比较。
利用美国国家卫生统计中心 2007 年全国出生率文件，我们比较了 LMP 早产和过期出生率以及特定胎龄的出生体重百分位数，与一个参考子集的出生情况进行对比，在参考子集中，胎龄分类错误的可能性最小。在使用正态混合模型、两种截断方法和临床/产科估计的胎龄进行校正之前和之后，对其进行了比较。
混合模型分别将早产和过期出生率校正了 90%和 41%，但之前的方法表现不佳。在纠正第 50 和第 90 个出生体重百分位数方面，混合模型也表现出色，在 28 至 36 孕周之间，其错误减少率在 68%至 85%之间，而大部分错误发生在这一区间。
混合模型的表现始终优于截断方法，特别是在 28 至 36 孕周之间。
