Bayesian Shape Invariant growth curve model for longitudinal data

Bayesian Shape Invariant growth curve model for longitudinal data

Bibliographic Details
Main Author: Bhuiyan, Mohammad AN
Language:English
Published: University of Cincinnati / OhioLINK 2019
Subjects:
Biostatistics
shape invariant
longitudinal
Growth curve
case-crossover
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ucin1561393650064101
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ucin15613936500641012021-08-16T05:10:40Z Bayesian Shape Invariant growth curve model for longitudinal data Bhuiyan, Mohammad AN Biostatistics shape invariant longitudinal Growth curve case-crossover The analysis of growth curves has played a vital role in estimating the growth trajectory of populations as well as identifying critical factors corresponding to various shapes of those trajectories. In recent years, shape invariant modeling has become an active area of research for non-parametric growth curve modeling, where a single function is transformed by scaling and shifting it to fit each subject usually through affine transformations. Lawton, first proposed SIM called it self-modeling regression; in their approach, the function for the underlying shape is illustrated for various parametric functions. Later, Beath developed a model to explain longitudinal growth patterns and extended the SIM to include time-dependent covariates. As a type of SIM, the regression spline expressed as a basis function consisting of a different set of knots; the resulting structure fitted as a nonlinear mixed effects model and parameters are typically estimated using maximum likelihood. This allows estimating the parameters for the between-subjects variation. The research in longitudinal growth curve modeling utilizing the Bayesian inferential procedure is limited, and the wider application is hindered by the computational complexities involved in such models. Cole proposed Super Imposition by Translation and Rotation model and expressed individual growth curves through three subject-specific parameters; named as size, tempo, and velocity. It is an important inferential problem to test no association between two binary variables based on data. A Test-based on sample odds ratio is commonly used. We bring in a competing test based on the Pearson correlation coefficient. An Odds ratio does not extend to higher order contingency tables, whereas the Pearson correlation does. It is a useful exercise to understand how the Pearson correlation stacks against the odds ratio in 2x2 tables. Another measure of association is the canonical correlation. In my second chapter we used power comparisons in 2x2 Contingency Tables: Odds Ratio versus Pearson Correlation versus Canonical Correlation to understand how Pearson correlation stacks against the odds ratio in 2x2 tables in the test of association.Air pollution is a growing global challenge and may have a moderate to the severe negative impact on human health. Vehicles, households, and industries emit a complex mixture of air pollutants, within which ambient particulate matter smaller than 2.5 micro m PM2.5 are thought to have the greatest effect on human health. Prior epidemiologic evidence suggests short-term PM2.5 exposure is associated with the development and exacerbation of several health problems. Children are more susceptible to PM2.5 related health effects due to their immature immune system and ongoing development and growth. The relationship of PM2.5 with asthma emergency department visit between 2011 and 2015 was identified within the Cincinnati Children’s Hospital Medical Center electronic medical record based on International Classification of Disease (ICD-9) and we used a data-driven clustering algorithm to find any clustering patterns existed by day within the study duration. In finding the impact of PM2.5 on stoke. we used a case-crossover design, to examine the association of exposure to PM2.5 and onset of incident stroke for the calendar year 2010. 2019-10-10 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1561393650064101 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1561393650064101 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Biostatistics
shape invariant
longitudinal
Growth curve
case-crossover
spellingShingle Biostatistics
shape invariant
longitudinal
Growth curve
case-crossover
Bhuiyan, Mohammad AN
Bayesian Shape Invariant growth curve model for longitudinal data
author Bhuiyan, Mohammad AN
author_facet Bhuiyan, Mohammad AN
author_sort Bhuiyan, Mohammad AN
title Bayesian Shape Invariant growth curve model for longitudinal data
title_short Bayesian Shape Invariant growth curve model for longitudinal data
title_full Bayesian Shape Invariant growth curve model for longitudinal data
title_fullStr Bayesian Shape Invariant growth curve model for longitudinal data
title_full_unstemmed Bayesian Shape Invariant growth curve model for longitudinal data
title_sort bayesian shape invariant growth curve model for longitudinal data
publisher University of Cincinnati / OhioLINK
publishDate 2019
url http://rave.ohiolink.edu/etdc/view?acc_num=ucin1561393650064101
work_keys_str_mv AT bhuiyanmohammadan bayesianshapeinvariantgrowthcurvemodelforlongitudinaldata
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