Special Issue Paper
Bayesian hierarchical modeling for a non-randomized, longitudinal fall prevention trial with spatially correlated observations
Article first published online: 4 FEB 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine
Special Issue: 13th Biennial CDC & ATSDR Symposium on Statistical Methods Info-Fusion: Utilization of Multi-Source Data
Volume 30, Issue 5, pages 522–530, 28 February 2011
How to Cite
Murphy, T. E., Allore, H. G., Leo-Summers, L. and Carlin, B. P. (2011), Bayesian hierarchical modeling for a non-randomized, longitudinal fall prevention trial with spatially correlated observations. Statist. Med., 30: 522–530. doi: 10.1002/sim.3912
- Issue published online: 10 FEB 2011
- Article first published online: 4 FEB 2011
- Manuscript Received: 11 FEB 2010
- Manuscript Accepted: 11 FEB 2010
- Bayesian hierarchical model;
- posterior predictive simulation;
- spatial residuals;
- non-randomized trial;
- longitudinal study;
- fall prevention
Because randomization of participants is often not feasible in community-based health interventions, non-randomized designs are commonly employed. Non-randomized designs may have experimental units that are spatial in nature, such as zip codes that are characterized by aggregate statistics from sources like the U.S. census and the Centers for Medicare and Medicaid Services. A perennial concern with non-randomized designs is that even after careful balancing of influential covariates, bias may arise from unmeasured factors. In addition to facilitating the analysis of interventional designs based on spatial units, Bayesian hierarchical modeling can quantify unmeasured variability with spatially correlated residual terms. Graphical analysis of these spatial residuals demonstrates whether variability from unmeasured covariates is likely to bias the estimates of interventional effect.
The Connecticut Collaboration for Fall Prevention is the first large-scale longitudinal trial of a community-wide healthcare intervention designed to prevent injurious falls in older adults. Over a two-year evaluation phase, this trial demonstrated a rate of fall-related utilization at hospitals and emergency departments by persons 70 years and older in the intervention area that was 11 per cent less than that of the usual care area, and a 9 per cent lower rate of utilization from serious injuries. We describe the Bayesian hierarchical analysis of this non-randomized intervention with emphasis on its spatial and longitudinal characteristics. We also compare several models, using posterior predictive simulations and maps of spatial residuals. Copyright © 2011 John Wiley & Sons, Ltd.