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- Creators: Arizona State University
- Creators: Grimm, Kevin
Description
When analyzing longitudinal data it is essential to account both for the correlation inherent from the repeated measures of the responses as well as the correlation realized on account of the feedback created between the responses at a particular time and the predictors at other times. A generalized method of moments (GMM) for estimating the coefficients in longitudinal data is presented. The appropriate and valid estimating equations associated with the time-dependent covariates are identified, thus providing substantial gains in efficiency over generalized estimating equations (GEE) with the independent working correlation. Identifying the estimating equations for computation is of utmost importance. This paper provides a technique for identifying the relevant estimating equations through a general method of moments. I develop an approach that makes use of all the valid estimating equations necessary with each time-dependent and time-independent covariate. Moreover, my approach does not assume that feedback is always present over time, or present at the same degree. I fit the GMM correlated logistic regression model in SAS with PROC IML. I examine two datasets for illustrative purposes. I look at rehospitalization in a Medicare database. I revisit data regarding the relationship between the body mass index and future morbidity among children in the Philippines. These datasets allow us to compare my results with some earlier methods of analyses.
ContributorsYin, Jianqiong (Author) / Wilson, Jeffrey Wilson (Thesis advisor) / Reiser, Mark R. (Committee member) / Kao, Ming-Hung (Committee member) / Arizona State University (Publisher)
Created2012
Description
Previous research has shown functional mixed-effects models and traditional mixed-effects models perform similarly when recovering mean and individual trajectories (Fine, Suk, & Grimm, 2019). However, Fine et al. (2019) showed traditional mixed-effects models were able to more accurately recover the underlying mean curves compared to functional mixed-effects models. That project generated data following a parametric structure. This paper extended previous work and aimed to compare nonlinear mixed-effects models and functional mixed-effects models on their ability to recover underlying trajectories which were generated from an inherently nonparametric process. This paper introduces readers to nonlinear mixed-effects models and functional mixed-effects models. A simulation study is then presented where the mean and random effects structure of the simulated data were generated using B-splines. The accuracy of recovered curves was examined under various conditions including sample size, number of time points per curve, and measurement design. Results showed the functional mixed-effects models recovered the underlying mean curve more accurately than the nonlinear mixed-effects models. In general, the functional mixed-effects models recovered the underlying individual curves more accurately than the nonlinear mixed-effects models. Progesterone cycle data from Brumback and Rice (1998) were then analyzed to demonstrate the utility of both models. Both models were shown to perform similarly when analyzing the progesterone data.
ContributorsFine, Kimberly L (Author) / Grimm, Kevin J. (Thesis advisor) / Edward, Mike (Committee member) / O'Rourke, Holly (Committee member) / McNeish, Dan (Committee member) / Arizona State University (Publisher)
Created2019
Description
This paper investigates a relatively new analysis method for longitudinal data in the framework of functional data analysis. This approach treats longitudinal data as so-called sparse functional data. The first section of the paper introduces functional data and the general ideas of functional data analysis. The second section discusses the analysis of longitudinal data in the context of functional data analysis, while considering the unique characteristics of longitudinal data such, in particular sparseness and missing data. The third section introduces functional mixed-effects models that can handle these unique characteristics of sparseness and missingness. The next section discusses a preliminary simulation study conducted to examine the performance of a functional mixed-effects model under various conditions. An extended simulation study was carried out to evaluate the estimation accuracy of a functional mixed-effects model. Specifically, the accuracy of the estimated trajectories was examined under various conditions including different types of missing data and varying levels of sparseness.
ContributorsWard, Kimberly l (Author) / Suk, Hye Won (Thesis advisor) / Aiken, Leona (Committee member) / Grimm, Kevin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Fall accident is a significant problem associated with our society both in terms of economic losses and human suffering [1]. In 2016, more than 800,000 people were hospitalized and over 33,000 deaths resulted from falling. Health costs associated with falling in 2016 yielded at 33% of total medical expenses in the US- mounting to approximately $31 billion per year. As such, it is imperative to find intervention strategies to mitigate deaths and injuries associated with fall accidents. In order for this goal to be realized, it is necessary to understand the mechanisms associated with fall accidents and more specifically, the movement profiles that may represent the cogent behavior of the locomotor system that may be amendable to rehabilitation and intervention strategies. In this light, this Thesis is focused on better understanding the factors influencing dynamic stability measure (as measured by Lyapunov exponents) during over-ground ambulation utilizing wireless Inertial Measurement Unit (IMU).
Four pilot studies were conducted: the First study was carried out to verify if IMU system was sophisticated enough to determine different load-carrying conditions. Second, to test the effects of walking inclinations, three incline levels on gait dynamic stability were examined. Third, tested whether different sections from the total gait cycle can be stitched together to assess LDS using the laboratory collected data. Finally, the fourth study examines the effect of “stitching” the data on dynamic stability measure from a longitudinally assessed (3-day continuous data collection) data to assess the effects of free-range data on assessment of dynamic stability.
Results indicated that load carrying significantly influenced dynamic stability measure but not for the floor inclination levels – indicating that future use of such measure should further implicate normalization of dynamic stability measures associated with different activities and terrain conditions. Additionally, stitching method was successful in obtaining dynamic stability measure utilizing free-living IMU data.
Four pilot studies were conducted: the First study was carried out to verify if IMU system was sophisticated enough to determine different load-carrying conditions. Second, to test the effects of walking inclinations, three incline levels on gait dynamic stability were examined. Third, tested whether different sections from the total gait cycle can be stitched together to assess LDS using the laboratory collected data. Finally, the fourth study examines the effect of “stitching” the data on dynamic stability measure from a longitudinally assessed (3-day continuous data collection) data to assess the effects of free-range data on assessment of dynamic stability.
Results indicated that load carrying significantly influenced dynamic stability measure but not for the floor inclination levels – indicating that future use of such measure should further implicate normalization of dynamic stability measures associated with different activities and terrain conditions. Additionally, stitching method was successful in obtaining dynamic stability measure utilizing free-living IMU data.
ContributorsMoon, Seong Hyun (Author) / Lockhart, Thurmon Eddy (Thesis advisor) / Lee, Hyunglae (Committee member) / Honeycutt, Claire (Committee member) / Arizona State University (Publisher)
Created2017
Description
Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together into nodes. LRP does this by fitting a mixed-effects model to each node every time that it becomes partitioned and extracting the deviance, which is the measure of node purity. LRP is implemented using the classification and regression tree algorithm, which suffers from a variable selection bias and does not guarantee reaching a global optimum. Additionally, fitting mixed-effects models to each potential split only to extract the deviance and discard the rest of the information is a computationally intensive procedure. Therefore, in this dissertation, I address the high computational demand, variable selection bias, and local optimum solution. I propose three approximation methods that reduce the computational demand of LRP, and at the same time, allow for a straightforward extension to recursive partitioning algorithms that do not have a variable selection bias and can reach the global optimum solution. In the three proposed approximations, a mixed-effects model is fit to the full data, and the growth curve coefficients for each individual are extracted. Then, (1) a principal component analysis is fit to the set of coefficients and the principal component score is extracted for each individual, (2) a one-factor model is fit to the coefficients and the factor score is extracted, or (3) the coefficients are summed. The three methods result in each individual having a single score that represents the growth curve trajectory. Therefore, now that the outcome is a single score for each individual, any tree-based method may be used for partitioning the data and group the individuals together. Once the individuals are assigned to their final nodes, a mixed-effects model is fit to each terminal node with the individuals belonging to it.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
ContributorsStegmann, Gabriela (Author) / Grimm, Kevin (Thesis advisor) / Edwards, Michael (Committee member) / MacKinnon, David (Committee member) / McNeish, Daniel (Committee member) / Arizona State University (Publisher)
Created2019