Extended growth curve models with random-effects covariance structures

1996 ◽  
Vol 25 (3) ◽  
pp. 571-584 ◽  
Author(s):  
Takahisa Yokoyama
2020 ◽  
Vol 15 (3) ◽  
pp. 2387-2393
Author(s):  
Mojtaba Ganjali ◽  
Taban Baghfalaki ◽  
Adeniyi Francis Fagbamigbe

Growth curve data consist of repeated measurements of a continuous growth process of human, animal, plant, microbial or bacterial genetic data over time in a population of individuals. A classical approach for analysing such data is the use of non-linear mixed effects models under normality assumption for the responses. But, sometimes the underlying population that the sample is extracted from is an abnormal population or includes some homogeneous sub-samples. So, detection of original properties of the population is an important scientific question of interest. In this paper, a sensitivity analysis of using different parametric and non-parametric distributions for the random effects on the results of applying non-linear mixed models is proposed for emphasizing the possible heterogeneity in the population. A Bayesian MCMC procedure is developed for parameter estimation and inference is performed via a hierarchical Bayesian framework. The methodology is illustrated using a real data set on study of influence of menarche on changes in body fat accretion.


2007 ◽  
Vol 98 (2) ◽  
pp. 317-327 ◽  
Author(s):  
Wai-Cheung Ip ◽  
Mi-Xia Wu ◽  
Song-Gui Wang ◽  
Heung Wong

2009 ◽  
Vol 66 (1) ◽  
pp. 84-89
Author(s):  
Suely Ruiz Giolo ◽  
Robin Henderson ◽  
Clarice Garcia Borges Demétrio

Cattle breeding programmes need objective criteria in order to evaluate and subsequently improve production systems. This work uses a logistic growth curve model for evaluating sires based on their progeny weight measured repeatedly over time. The parameters of the curve are described as a linear function of fixed and random effects. A Bayesian approach is used for the estimation. Analysis of the weights recorded on animals of the Nellore breed shows that growth curve models with fixed and random effects can be useful to evaluate and selecting sires.


2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A64.1-A64
Author(s):  
Charlotta Nilsen ◽  
Ross Andel ◽  
Alexander Darin-Mattsson ◽  
Ingemar Kåreholt

ObjectivesThe demographic shift towards an aging society has made it important to understand underlying life course trajectories of later life health and function. The aim was to investigate if psychosocial working conditions are associated with later life physical function.MethodTwo individually linked longitudinal Swedish surveys were used (n=803). A psychosocial job exposure matrix was used to measure psychosocial working conditions in the first occupation and at ages 25, 30, 35, 40, 45, and 50 - based on work history - and current occupation at baseline (1991). Physical function was measured in 2014. Random effects growth curve models were used to calculate within-person change. Random effects growth curve models were used to calculate intraindividual trajectories of working conditions, analyzed in relation to functional impairment with ordered logistic regression.ResultsHaving a more active job at baseline was associated with decreased odds of functional impairment in old age (OR 0.87, CI 0.76–0.99). Having a more high strain job at baseline was associated with increased odds of functional impairment in old age (OR 1.33, CI 1.04–1.70). Having a high starting point and upward trajectory of job strain throughout working life were associated with increased odds of functional impairment in old age (OR 3.16, CI 1.73–5.80).DiscussionPromoting a healthy workplace by reducing chronic stress and inducing intellectual stimulation, control, and personal growth, may not only improve the health of workers. It may also lower the cost of health and social care by improving health and function of the older population. Hence, investing in a healthy workplace should be seen as a double-win investment for society.


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