Any views expressed within media held on this service are those of the contributors, should not be taken as approved or endorsed by the University, and do not necessarily reflect the views of the University in respect of any particular issue.
A mixture model to correct misclassification of gestational age

A mixture model to correct misclassification of gestational age


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.



Report this page

To report inappropriate content on this page, please use the form below. Upon receiving your report, we will be in touch as per the Take Down Policy of the service.

Please note that personal data collected through this form is used and stored for the purposes of processing this report and communication with you.

If you are unable to report a concern about content via this form please contact the Service Owner.

Please enter an email address you wish to be contacted on. Please describe the unacceptable content in sufficient detail to allow us to locate it, and why you consider it to be unacceptable.
By submitting this report, you accept that it is accurate and that fraudulent or nuisance complaints may result in action by the University.