Journal article
Statistical methods for constructing gestational age-related charts for fetal size and pregnancy dating using longitudinal data
Biocybernetics and biomedical engineering, Vol.38(4), pp.992-1003
2018
DOI: 10.1016/j.bbe.2018.09.001
Abstract
The assessment of fetal size and the accurate estimation of gestational age are of crucial importance for proper pregnancy management. The information is almost exclusively based on ultrasound measurements of fetal biometric parameters and the means for evaluating these measurements are age-related reference charts (centile charts) allowing interpretation of obtained fetal measurement in comparison with the expected average measurement in the reference population. The construction of such reference charts requires an appropriate statistical methodology. The most frequent method for the construction of fetal reference charts from cross-sectional data is the parametric approach with fractional polynomials regression functions for the mean and standard deviation of each fetal measurement. This article suggests how this method can be extended to longitudinal data using fractional polynomials in linear mixed effect regression. The presented approach includes maximum likelihood estimation for fitting first- and second-order fractional polynomial models, and multimodel inference using Akaike's information criterion and related tools as a suitable strategy for model selection. Finally, an example of the suggested approach is presented.
Details
- Title: Subtitle
- Statistical methods for constructing gestational age-related charts for fetal size and pregnancy dating using longitudinal data
- Creators
- Martin Hynek - GennetJan Kalina - Czech Academy of SciencesJana Zvárová - Czech Academy of SciencesJeffrey D Long - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Biocybernetics and biomedical engineering, Vol.38(4), pp.992-1003
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.bbe.2018.09.001
- ISSN
- 0208-5216
- Language
- English
- Date published
- 2018
- Academic Unit
- Psychiatry; Biostatistics
- Record Identifier
- 9984280865802771
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