Analysis of beef cattle longitudinal data applying a nonlinear model

The objective of this work was to evaluate the Nelore beef cattle, growth curve parameters using the Von Bertalanffy function in a nested Bayesian procedure that allowed estimation of the joint posterior distribution of growth curve parameters, their (co)variance components, and the environmental and additive genetic components affecting them. A hierarchical model was applied; each individual had a growth trajectory described by the nonlinear function, and each parameter of this function was considered to be affected by genetic and environmental effects that were described by an animal model. Random samples of the posterior distributions were drawn using Gibbs sampling and Metropolis-Hastings algorithms. The data set consisted of a total of 145,961 BW recorded from 15,386 animals. Even though the curve parameters were estimated for animals with few records, given that the information from related animals and the structure of systematic effects were considered in the curve fitting, all mature BW predicted were suitable. A large additive genetic variance for mature BW was observed. The parameter a of growth curves, which represents asymptotic adult BW, could be used as a selection criterion to control increases in adult BW when selecting for growth rate. The effect of maternal environment on growth was carried through to maturity and should be considered when evaluating adult BW. Other growth curve parameters showed small additive genetic and maternal effects. Mature BW and parameter k, related to the slope of the curve, presented a large, positive genetic correlation. The results indicated that selection for growth rate would increase adult BW without substantially changing the shape of the growth curve. Selection to change the slope of the growth curve without modifying adult BW would be inefficient because their genetic correlation is large. However, adult BW could be considered in a selection index with its corresponding economic weight to improve the overall efficiency of beef cattle production.

Bayesian longitudinal data analysis with mixed models and thick-tailed distributions using MCMC

Linear mixed effects models are frequently used to analyse longitudinal data, due to their flexibility in modelling the covariance structure between and within observations. Further, it is easy to deal with unbalanced data, either with respect to the number of observations per subject or per time period, and with varying time intervals between observations. In most applications of mixed models to biological sciences, a normal distribution is assumed both for the random effects and for the residuals. This, however, makes inferences vulnerable to the presence of outliers. Here, linear mixed models employing thick-tailed distributions for robust inferences in longitudinal data analysis are described. Specific distributions discussed include the Student-t, the slash and the contaminated normal. A Bayesian framework is adopted, and the Gibbs sampler and the Metropolis-Hastings algorithms are used to carry out the posterior analyses. An example with data on orthodontic distance growth in children is discussed to illustrate the methodology. Analyses based on either the Student-t distribution or on the usual Gaussian assumption are contrasted. The thick-tailed distributions provide an appealing robust alternative to the Gaussian process for modelling distributions of the random effects and of residuals in linear mixed models, and the MCMC implementation allows the computations to be performed in a flexible manner.

Wage differentials between the public and private sectors in Chile: Evidence from longitudinal data

Includes bibliography.

Competing regression models for longitudinal data

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretestposttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses.

Longitudinal data on telomere length in leukocytes from newborn baboons support a marked drop in stem cell turnover around 1 year of age

Stem cells of various tissues are typically defined as multipotent cells with 'self-renewal' properties. Despite the increasing interest in stem cells, surprisingly little is known about the number of times stem cells can or do divide over a lifetime. Based on telomere-length measurements of hematopoietic cells, we previously proposed that the self-renewal capacity of hematopoietic stem cells is limited by progressive telomere attrition and that such cells divide very rapidly during the first year of life. Recent studies of patients with aplastic anemia resulting from inherited mutations in telomerase genes support the notion that the replicative potential of hematopoietic stem cells is directly related to telomere length, which is indirectly related to telomerase levels. To revisit conclusions about stem cell turnover based on cross-sectional studies of telomere length, we performed a longitudinal study of telomere length in leukocytes from newborn baboons. All four individual animals studied showed a rapid decline in telomere length (approximately 2-3 kb) in granulocytes and lymphocytes in the first year after birth. After 50-70 weeks the telomere length appeared to stabilize in all cell types. These observations suggest that hematopoietic stem cells, after an initial phase of rapid expansion, switch at around 1 year of age to a different functional mode characterized by a markedly decreased turnover rate.

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Dimensions and determinants of upward mobility : a study based on longitudinal data from Delhi slums

This study based on two primary surveys of the same households in two different years (2007/08 and 2012) assesses the extent of inter-temporal change in income of the individual workers and makes an attempt to identify the factors which explain upward mobility in alternate econometric framework, envisaging endogeneity problem. It also encompasses a host of indicators of wellbeing and constructs the transition matrix to capture the extent of change over time at the household level. The findings are indicative of a rise in the income of workers across a sizeable percentage of households though many of them remained below the poverty line notwithstanding this increase. In fact, there is a wide spread deterioration in the wellbeing index constructed at the household level. Among several determinants of income rise two important policy prescriptions can be elicited. Inadequate education reduces the probability of upward mobility while education above a threshold level raises it. Savings are crucial for upward mobility impinging on the importance of asset creation. Views that entail neighbourhood spill-over effects also received validation. Besides, investment in housing and basic amenities turns out to be crucial for improvement in wellbeing levels.

Recovery from Pediatric Brain Injury: A Preliminary Comparison of Longitudinal Data on Psychological Symptomology and Mental Health Treatment

Objective: To document the course of psychological symptomology, mental health treatment, and unmet psychological needs using caregiver reports in the first 18 months following pediatric brain injury (BI). Method: Participants included 28 children (aged 1-18 years) who were hospitalized at a children's hospital's rehabilitation unit. Caregiver reports of children's psychological symptoms, receipt of mental health treatment, and unmet psychological needs were assessed at one month, six months, 12 months, and 18 months post-BI. Results: Caregivers reported a general increase in psychological symptoms and receipt of mental health treatment over the 18 months following BI; however, there was a substantial gap between the high rate of reported symptoms and low rate of reported treatment. Across all four follow-up time points there were substantial unmet psychological needs (at least 60% of sample). Conclusions: Findings suggest that there are substantial unmet psychological needs among children during the first 18 months after BI. Barriers to mental health treatment for this population need to be addressed.

Individuals and families in transition : understanding change through longitudinal data : papers presented at the Social Science Research Council conference in Annapolis, Maryland, March 16-18, 1988 /

Mode of access: Internet.

A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants

The paper investigates a Bayesian hierarchical model for the analysis of categorical longitudinal data from a large social survey of immigrants to Australia. Data for each subject are observed on three separate occasions, or waves, of the survey. One of the features of the data set is that observations for some variables are missing for at least one wave. A model for the employment status of immigrants is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response and then subsequent terms are introduced to explain wave and subject effects. To estimate the model, we use the Gibbs sampler, which allows missing data for both the response and the explanatory variables to be imputed at each iteration of the algorithm, given some appropriate prior distributions. After accounting for significant covariate effects in the model, results show that the relative probability of remaining unemployed diminished with time following arrival in Australia.