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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

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

Suicide is a significant public health problem, with incidence rates and lethality continuing to increase yearly. Given the large human and financial cost of suicide worldwide alongside the lack of progress in suicide prediction, more research is needed to inform suicide prevention and intervention efforts. This study approaches suicide from

Suicide is a significant public health problem, with incidence rates and lethality continuing to increase yearly. Given the large human and financial cost of suicide worldwide alongside the lack of progress in suicide prediction, more research is needed to inform suicide prevention and intervention efforts. This study approaches suicide from the lens of suicide note-leaving behavior, which can provide important information on predictors of suicide. Specifically, this study adds to the existing literature on note-leaving by examining history of suicidality, mental health problems, and their interaction in predicting suicide note-leaving, in addition to demographic predictors of note-leaving examined in previous research using data from the National Violent Death Reporting System (NVDRS, n = 98,515). We fit a logistic regression model predicting leaving a suicide note or not, the results of which indicated that those with mental health problems or a history of suicidality were more likely to leave a suicide note than those without such histories, and those with both mental health problems and a history of suicidality were most likely to leave a suicide note. These findings reinforce the need to tailor suicide prevention efforts toward identifying and targeting higher risk populations.

ContributorsCarnesi, Gregory (Author) / O'Rourke, Holly (Thesis director) / Brewer, Gene (Committee member) / Corbin, William (Committee member) / Chassin, Laurie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Watts College of Public Service & Community Solut (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor)
Created2022-05
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ContributorsCarnesi, Gregory (Author) / O'Rourke, Holly (Thesis director) / Brewer, Gene (Committee member) / Corbin, William (Committee member) / Chassin, Laurie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2022-05
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ContributorsCarnesi, Gregory (Author) / O'Rourke, Holly (Thesis director) / Brewer, Gene (Committee member) / Corbin, William (Committee member) / Chassin, Laurie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor)
Created2022-05
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Created1925-19-39 (uncertain)
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Created1922
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Created1934
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Created1922
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Created1921
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Created1921