A comparison of methods for construction of fetal reference charts. (26th October 2015)

• Record Type: Journal Article
• Authors: Nevo, Daniel; Mandel, Micha; Ein‐Mor, Eliana; Shen, Ori; Chetrit, Avraham Ben; Daniel‐Spiegel, Etty; Yagel, Simcha
• Other Names: Heinze Georg guestEditor.; Michiels Stefan guestEditor.; Posch Martin guestEditor.
• Is Part Of: Statistics in medicine. Volume 35:Number 7(2016)
• Page Start: 1226
• Page End: 1240
• Publication Date: 2015-10-26
• Subjects: reference charts -- quantile estimation -- time‐dependent quantiles -- quantile regression; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6774
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Reference charts for fetal measures are used for early detection of pregnancies that should be monitored closely. Construction of reference charts corresponds to estimation of quantiles of a distribution as a function of gestational age. Existing methods have been developed under various modeling assumptions, typically by fitting a polynomial regression to certain functionals of the distributions (e.g., mean, standard deviation, and quantiles). We use a large dataset to compare various existing methods for construction of reference charts. We also relax the assumptions of a parametric polynomial link between the distribution parameters and age and consider cubic splines and discretization of age in order to compare charts based on more flexible and simpler models, respectively. We compare the different methods using various tools and demonstrate the importance of considering performance measures calculated from age‐stratified data. We also examine the question of sample size. We compare our charts to similar charts that have been recently published and emphasize that the source of an apparent heterogeneity should be investigated. We conclude that the choice of which method to use for construction of reference charts should take the following into account: available sample size, validity of normality assumption, and results of various performance measures. Copyright © 2015 John Wiley & Sons, Ltd.