Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients

In this paper, we present different ofrailtyo models to analyze longitudinal data in the presence of covariates. These models incorporate the extra-Poisson variability and the possible correlation among the repeated counting data for each individual. Assuming a CD4 counting data set in HIV-infected patients, we develop a hierarchical Bayesian analysis considering the different proposed models and using Markov Chain Monte Carlo methods. We also discuss some Bayesian discrimination aspects for the choice of the best model.

A BAYESIAN SHRINKAGE MODEL FOR INCOMPLETE LONGITUDINAL BINARY DATA WITH APPLICATION TO THE BREAST CANCER PREVENTION TRIAL

We consider inference in randomized studies, in which repeatedly measured outcomes may be informatively missing due to drop out. In this setting, it is well known that full data estimands are not identified unless unverified assumptions are imposed. We assume a non-future dependence model for the drop-out mechanism and posit an exponential tilt model that links non-identifiable and identifiable distributions. This model is indexed by non-identified parameters, which are assumed to have an informative prior distribution, elicited from subject-matter experts. Under this model, full data estimands are shown to be expressed as functionals of the distribution of the observed data. To avoid the curse of dimensionality, we model the distribution of the observed data using a Bayesian shrinkage model. In a simulation study, we compare our approach to a fully parametric and a fully saturated model for the distribution of the observed data. Our methodology is motivated and applied to data from the Breast Cancer Prevention Trial.

Longitudinal dyadic data analysis of stroke survivors and their spousal caregivers

Approximately 795,000 new and recurrent strokes occur each year. Because of the resulting functional impairment, stroke survivors are often discharged into the care of a family caregiver, most often their spouse. This dissertation explored the effect that mutuality, a measure of the perceived positive aspects of the caregiving relationship, had on the stress and depression of 159 stroke survivors and their spousal caregivers over the first 12 months post discharge from inpatient rehabilitation. Specifically, cross-lagged regression was utilized to investigate the dyadic, longitudinal relationship between caregiver and stroke survivor mutuality and caregiver and stroke survivor stress over time. Longitudinal meditational analysis was employed to examine the mediating effect of mutuality on the dyads’ perception of family function and caregiver and stroke survivor depression over time.^ Caregivers’ mutuality was found to be associated with their own stress over time but not the stress of the stroke survivor. Caregivers who had higher mutuality scores over the 12 months of the study had lower perceived stress. Additionally, a partner effect of stress for the stroke survivor but not the caregiver was found, indicating that stroke survivors’ stress over time was associated with caregivers’ stress but caregivers’ stress over time was not significantly associated with the stress of the stroke survivor.^ This dissertation did not find mutuality to mediate the relationship between caregivers’ and stroke survivors’ perception of family function at baseline and their own or their partners’ depression at 12 months as hypothesized. However, caregivers who perceived healthier family functioning at baseline and stroke survivors who had higher perceived mutuality at 12 months had lower depression at one year post discharge from inpatient rehabilitation. Additionally, caregiver mutuality at 6 months, but not at baseline or 12 months, was found to be inversely related to caregiver depression at 12 months.^ These findings highlight the interpersonal nature of stress in the context of caregiving, especially among spousal relationships. Thus, health professionals should encourage caregivers and stroke survivors to focus on the positive aspects of the caregiving relationship in order to mitigate stress and depression. ^

Longitudinal categorical data analysis: A continuous -time Markov chain approach

In this dissertation, we propose a continuous-time Markov chain model to examine the longitudinal data that have three categories in the outcome variable. The advantage of this model is that it permits a different number of measurements for each subject and the duration between two consecutive time points of measurements can be irregular. Using the maximum likelihood principle, we can estimate the transition probability between two time points. By using the information provided by the independent variables, this model can also estimate the transition probability for each subject. The Monte Carlo simulation method will be used to investigate the goodness of model fitting compared with that obtained from other models. A public health example will be used to demonstrate the application of this method. ^

Physiological changes in plasma proteins characteristic of human reproduction; cross-sectional and longitudinal electrophoretic data for women during and following uncomplicated and complicated pregnancies,

Includes bibliographies.

Methods for Categorical Longitudinal Survey Data: Understanding Employment Status of Australian Women

Many variables that are of interest in social science research are nominal variables with two or more categories, such as employment status, occupation, political preference, or self-reported health status. With longitudinal survey data it is possible to analyse the transitions of individuals between different employment states or occupations (for example). In the statistical literature, models for analysing categorical dependent variables with repeated observations belong to the family of models known as generalized linear mixed models (GLMMs). The specific GLMM for a dependent variable with three or more categories is the multinomial logit random effects model. For these models, the marginal distribution of the response does not have a closed form solution and hence numerical integration must be used to obtain maximum likelihood estimates for the model parameters. Techniques for implementing the numerical integration are available but are computationally intensive requiring a large amount of computer processing time that increases with the number of clusters (or individuals) in the data and are not always readily accessible to the practitioner in standard software. For the purposes of analysing categorical response data from a longitudinal social survey, there is clearly a need to evaluate the existing procedures for estimating multinomial logit random effects model in terms of accuracy, efficiency and computing time. The computational time will have significant implications as to the preferred approach by researchers. In this paper we evaluate statistical software procedures that utilise adaptive Gaussian quadrature and MCMC methods, with specific application to modeling employment status of women using a GLMM, over three waves of the HILDA survey.