Statistical methodology for constructing gestational age‐related charts using cross‐sectional and longitudinal data: The INTERGROWTH‐21st project as a case study
| العنوان: | Statistical methodology for constructing gestational age‐related charts using cross‐sectional and longitudinal data: The INTERGROWTH‐21st project as a case study |
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| المؤلفون: | Eric O Ohuma, Douglas G. Altman |
| المصدر: | Statistics in Medicine |
| بيانات النشر: | John Wiley and Sons Inc., 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Statistics and Probability, Special Issue Papers, longitudinal, Epidemiology, Computer science, Longitudinal data, Gestational age, Statistical model, 01 natural sciences, Regression, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Statistics, Special Issue Paper, statistical methodology, Intergrowth 21st, Fetal head, 030212 general & internal medicine, Model choice, cross‐sectional, 0101 mathematics, Raw data, human growth |
| الوصف: | Most studies aiming to construct reference or standard charts use a cross‐sectional design, collecting one measurement per participant. Reference or standard charts can also be constructed using a longitudinal design, collecting multiple measurements per participant. The choice of appropriate statistical methodology is important as inaccurate centiles resulting from inferior methods can lead to incorrect judgements about fetal or newborn size, resulting in suboptimal clinical care. Reference or standard centiles should ideally provide the best fit to the data, change smoothly with age (eg, gestational age), use as simple a statistical model as possible without compromising model fit, and allow the computation of Z‐scores from centiles to simplify assessment of individuals and enable comparison with different populations. Significance testing and goodness‐of‐fit statistics are usually used to discriminate between models. However, these methods tend not to be useful when examining large data sets as very small differences are statistically significant even if the models are indistinguishable on actual centile plots. Choosing the best model from amongst many is therefore not trivial. Model choice should not be based on statistical considerations (or tests) alone as sometimes the best model may not necessarily offer the best fit to the raw data across gestational age. In this paper, we describe the most commonly applied methodologies available for the construction of age‐specific reference or standard centiles for cross‐sectional and longitudinal data: Fractional polynomial regression, LMS, LMST, LMSP, and multilevel regression methods. For illustration, we used data from the INTERGROWTH‐21st Project, ie, newborn weight (cross‐sectional) and fetal head circumference (longitudinal) data as examples. |
| اللغة: | English |
| تدمد: | 1097-0258 0277-6715 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1810eb6c80ad12a2c59459e8805bae62Test http://europepmc.org/articles/PMC6767451Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....1810eb6c80ad12a2c59459e8805bae62 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10970258 02776715 |
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