title: A two-component Bayesian mixture model to identify implausible gestational age.
abstract: Background:Birth weight and gestational age are two important variables in obstetric research. The primary measure of gestational age is based on a mother's recall of her last menstrual period. This recall may cause random or systematic errors. Therefore, the objective of this study is to utilize Bayesian mixture model in order to identify implausible gestational age.Methods:In this cross-sectional study, medical documents of 502 preterm infants born and hospitalized in Hamadan Fatemieh Hospital from 2009 to 2013 were gathered. Preterm infants were classified to less than 28 weeks and 28 to 31 weeks. A two-component Bayesian mixture model was utilized to identify implausible gestational age; the first component shows the probability of correct and the second one shows the probability of incorrect classification of gestational ages. The data were analyzed through OpenBUGS 3.2.2 and 'coda' package of R 3.1.1.Results:The mean (SD) of the second component of less than 28 weeks and 28 to 31 weeks were 1179 (0.0123) and 1620 (0.0074), respectively. These values were larger than the mean of the first component for both groups which were 815.9 (0.0123) and 1061 (0.0074), respectively.Conclusion:Errors occurred in recording the gestational ages of these two groups of preterm infants included recording the gestational age less than the actual value at birth. Therefore, developing scientific methods to correct these errors is essential to providing desirable health services and adjusting accurate health indicators.
authors.affiliation: MSc, Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. info.amargiran@gmail.com.
authors.forename: Maryam
authors.initials: M
authors.lastname: Mohammadian-Khoshnoud
authors.affiliation: PhD, Department of Biostatistics, School of Public Health, Modeling of Noncommunicable Disease Research Canter, Hamadan University of Medical Sciences, Hamadan, Iran. moghimbeigi@umsha.ac.ir.
authors.forename: Abbas
authors.initials: A
authors.lastname: Moghimbeigi
authors.affiliation: PhD, Department of Biostatistics, School of Public Health, Modeling of Noncommunicable Disease Research Canter, Hamadan University of Medical Sciences, Hamadan, Iran. javad.faradmal@umsha.ac.ir.
authors.forename: Javad
authors.initials: J
authors.lastname: Faradmal
authors.affiliation: MD, Department of Gynecology, Hamadan University of Medical Sciences, Hamadan, Iran. m_yavangui@yahoo.com.
authors.forename: Mahnaz
authors.initials: M
authors.lastname: Yavangi
keywords: Bayesian; Birth weight; Gestational age; Mixture model