Journal Title
Title of Journal: Health Serv Outcomes Res Method
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Abbravation: Health Services and Outcomes Research Methodology
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Authors: M Gebregziabher Y Zhao C E Dismuke N Axon K J Hunt L E Egede
Publish Date: 2012/11/11
Volume: 13, Issue: 1, Pages: 39-57
Abstract
The common approach to modeling healthcare cost data is to use aggregated total cost from multiple categories or sources eg inpatient outpatient prescriptions etc as the dependent variable However this approach could hide the differential impact of covariates on the different cost categories An alternative is to model each cost category separately This could also lead to wrong conclusions due to failure to account for the interdependence among the multiple cost outcomes Therefore we propose a multivariate generalized linear mixed model mGLMM that allows for joint modeling of longitudinal cost data from multiple sources We assessed four different approaches 1 shared random intercept 2 shared random intercept and slope 3 separate random intercepts from a joint multivariate distribution and 4 separate random intercepts and slopes from a joint multivariate distribution Each of these approaches differs in the way they account for the correlation among the multiple cost outcomes Comparison was made via goodness of fit measures and residual plots Longitudinal cost data from a national cohort of 740195 veterans with diabetes followed from 2002–2006 was used to demonstrate joint modeling Among examined models the separate random intercept approach exhibited the lowest AIC/BIC in both lognormal and gamma GLMMs However for our data example the shared random intercept approach seemed to be sufficient as the more complex models did not lead to qualitatively different conclusionsWe acknowledge and appreciate the resources provided by the Center for Disease Prevention and Health Interventions for Diverse Populations HSRD program Grant REA 08261 and the Ralph H Johnson Veterans Affairs Medical Center The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans AffairsAfter arranging the data as in Table 1 of the paper then the following SAS codes can be used to implement the four approaches each of these could be run with or without the Rside covariance matrix specification given with the 2nd random statement Open image in new window
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