A Bayesian model averaging approach to examining changes in quality of life among returning Iraq and Afghanistan veterans

Abstract

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.

Keywords: Bayesian analysis; Veterans; mental health; quality of life.

Conflict of interest statement

The authors have no competing interests.

Copyright © 2014 John Wiley & Sons, Ltd.

Figures

Figure 1

Posterior density plots of predictors for four‐month QoLS with posterior probabilities of being non‐zero > 20% outlined below (N = 117).

Figure 2

Posterior density plots of predictors for eight‐month QoLS with posterior probabilities of being non‐zero > 20% outlined below (N = 117).