A Mixture Model to Correct Misclassification of Gestational Age

摘要

Purpose: 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. Methods: 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. Results: 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. Conclusions: The mixture model behaved consistently better than truncation methods, particularly between weeks 28 and 36 of gestation.