Organismic Growth: P-Technique in the Analysis of Longitudinal Growth Data

1954 ◽  
Vol 25 (2) ◽  
pp. 83 ◽  
Author(s):  
Fred T. Tyler
Keyword(s):  
Longitudinal Growth ◽  
Growth Data

scholarly journals Pubertal growth in normal Thai children: a longitudinal study

2010 ◽  
Vol 4 (5) ◽  
pp. 793-795
Author(s):  
Suttipong Wacharasindhu ◽  
Vichit Supornsilchai ◽  
Suphab Aroonparkmongkol ◽  
Thaninee Sahakitrungrueng
Keyword(s):  
Longitudinal Study ◽  
Growth Parameters ◽  
Longitudinal Growth ◽  
Compensatory Mechanism ◽  
Growth Data ◽  
Clinical Practices ◽  
Normal Children ◽  
Pubertal Onset ◽  
Pubertal Growth ◽  
Height Gain

Abstract Background: Pubertal growth data in Thai children has been reported as cross-sectional studies. There is no longitudinal study in Thai children. Objective: Investigate the longitudinal growth data in normal Thai children including the relationship between age at pubertal onset and other growth parameters. Material and method: Eighty-eight normal children (44 boys, 44 girls) were longitudinally assessed for the growth and puberty until they reached their final adult height. Pubertal staging was assessed by the Tanner method. Results: Mean age of pubertal onset was 10.2 ± 1.2 years for girls and 12.2 ± 1.0 years for boys. Total pubertal height gain was 18.3 ± 4.0 cm for girls and 22.3 ± 4.4 cm for boys. Total pubertal height gain had a negative correlation with age at pubertal onset for girls, but not for boys. Conclusion: The onset of puberty was not much changed from previous studies. Girls with early puberty had a higher pubertal height gain. This might be due to a compensatory mechanism. These longitudinal growth data can be used as a reference in clinical practices for Thai children.

The impact of random-effect misspecification on percentile estimation for longitudinal growth data

2012 ◽  
Vol 31 (28) ◽  
pp. 3708-3718 ◽  
Author(s):  
Kyeongmi Cheon ◽  
Paul S. Albert ◽  
Zhiwei Zhang
Keyword(s):  
Random Effect ◽  
Longitudinal Growth ◽  
Growth Data ◽  
Percentile Estimation ◽  
The Impact

scholarly journals Multilevel growth curve models that incorporate a random coefficient model for the level 1 variance function

2017 ◽  
Vol 27 (11) ◽  
pp. 3478-3491 ◽  
Author(s):  
Harvey Goldstein ◽  
George Leckie ◽  
Christopher Charlton ◽  
Kate Tilling ◽  
William J Browne
Keyword(s):  
Random Effects ◽  
Repeated Measures ◽  
Random Effect ◽  
Growth Function ◽  
Individual Variability ◽  
Growth Patterns ◽  
Variance Function ◽  
Longitudinal Growth ◽  
Growth Data ◽  
The Mean

Aim To present a flexible model for repeated measures longitudinal growth data within individuals that allows trends over time to incorporate individual-specific random effects. These may reflect the timing of growth events and characterise within-individual variability which can be modelled as a function of age. Subjects and methods A Bayesian model is developed that includes random effects for the mean growth function, an individual age-alignment random effect and random effects for the within-individual variance function. This model is applied to data on boys’ heights from the Edinburgh longitudinal growth study and to repeated weight measurements of a sample of pregnant women in the Avon Longitudinal Study of Parents and Children cohort. Results The mean age at which the growth curves for individual boys are aligned is 11.4 years, corresponding to the mean ‘take off’ age for pubertal growth. The within-individual variance (standard deviation) is found to decrease from 0.24 cm2 (0.50 cm) at 9 years for the ‘average’ boy to 0.07 cm2 (0.25 cm) at 16 years. Change in weight during pregnancy can be characterised by regression splines with random effects that include a large woman-specific random effect for the within-individual variation, which is also correlated with overall weight and weight gain. Conclusions The proposed model provides a useful extension to existing approaches, allowing considerable flexibility in describing within- and between-individual differences in growth patterns.

London Archive of Longitudinal Growth Data

1986 ◽  
Vol 13 (2) ◽  
pp. 179-182
Author(s):  
J.M. Tanner ◽  
L.A. Cox
Keyword(s):  
Longitudinal Growth ◽  
Growth Data

scholarly journals Feeding Intolerance and Poor Growth in Infants with Gastroschisis: Longitudinal Experience with Consecutive Patients over Thirteen Years

2015 ◽  
Vol 4 (4) ◽  
pp. 42 ◽  
Author(s):  
Saloni Balgi ◽  
Sarita Singhal ◽  
Georgia Mueller ◽  
Beau Batton
Keyword(s):  
Weight Gain ◽  
Linear Regression ◽  
Hospital Length Of Stay ◽  
Longitudinal Growth ◽  
Feeding Intolerance ◽  
Growth Data ◽  
Term Infants ◽  
Poor Growth ◽  
Factors Associated ◽  
Hospital Factors

Objectives: 1) To investigate in-hospital factors associated with delayed tolerance of full volume enteral nutrition and 2) To assess longitudinal growth in a contemporary population of infants with gastroschisis.Design: Retrospective single-center study of all infants with gastroschisisSetting: Level III neonatal intensive care unit in a free-standing Children’s HospitalDuration: 13.5 yearsMaterials & Methods: Detailed data regarding demographics, nutritional support, growth, and infant outcomes was collected for all infants with gastroschisis. Linear regression was used to investigate in-hospital factors associated with feeding intolerance and poor growth.Results: For 52 infants, the median gestational age at birth was 36 weeks, the median postnatal age to achieve full feeds was 22 days, and median in-hospital weight gain was 18 gm/day. With linear regression, there was a positive association between time to full feeds and both hospital length of stay (adjusted R2=0.503, p<0.0001) and (unexpectedly) in-hospital weight gain (adjusted R2=0.125, p=0.0248). There was a negative association between in-hospital weight gain and preterm birth (adjusted R2=0.125, p=0.0356). For infants with longitudinal growth data, 35% had a weight <5th percentile (of whom 67% were preterm).Conclusions: Many infants with gastroschisis have poor growth before and after hospital discharge. Aggressive feeding advancement may be a contributing factor to this finding and preterm infants may be at greater risk for poor growth than term infants.

Methods for analysis of unbalanced, longitudinal, growth data

1992 ◽  
Vol 28 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Lincoln E. Moses ◽  
Lynn C. Gale ◽  
Jeanne Altmann
Keyword(s):  
Longitudinal Growth ◽  
Growth Data
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