A Bayesian model averaging approach to examining changes in quality of life among returning Iraq and Afghanistan veterans.
A Bayesian model averaging approach to examining changes in quality of life among returning Iraq and Afghanistan veterans.
复制标题
贝叶斯模型平均方法用于检查返回的伊拉克和阿富汗退伍军人的生活质量变化。
DOI：
10.1002/mpr.1442
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发表时间：
2014
影响因子：
3.1
通讯作者：
Morissette,SandraB
中科院分区：
文献类型：
--
作者：
Stock,EileenM;Kimbrel,NathanA;Meyer,EricC;Copeland,LaurelA;Monte,Ralph;Zeber,JohnE;Gulliver,SuzyBird;Morissette,SandraB
Many Veterans from the conflicts in Iraq and Afghanistan return home with physical and psychological impairments that impact their ability to enjoy normal life activities and diminish their quality of life (QoL). The present research aimed to identify predictors of QoL over an eight‐month period using Bayesian model averaging (BMA), which is a statistical technique useful for maximizing power with smaller sample sizes. A sample of 117 Iraq and Afghanistan Veterans receiving care in a southwestern health care system was recruited, and BMA examined the impact of key demographics (e.g. age, gender), diagnoses (e.g. depression), and treatment modalities (e.g. individual therapy, medication) on QoL over time. Multiple imputation based on Gibbs sampling was employed for incomplete data (6.4% missingness). Average follow‐up QoL scores were significantly lower than at baseline (73.2 initial versus 69.5 four‐month and 68.3 eight‐month). Employment was associated with increased QoL during each follow‐up, while post‐traumatic stress disorder and Black race were inversely related. Additionally, predictive models indicated that depression, income, treatment for a medical condition, and group psychotherapy were strong negative predictors of four‐month QoL but not eight‐month QoL.Copyright © 2014 John Wiley & Sons, Ltd.
