Nabilone as an adjunctive to GBP is an effective, well-tolerated combination for MS-induced NPP. The results of this study identify a novel therapeutic combination for use in this population of patients predisposed to tolerability issues that may otherwise prevent effective pain management.
Previously, small area estimation under a nested error linear regression model was studied with area level covariates subject to measurement error. However, the information on observed covariates was not used in finding the Bayes predictor of a small area mean. In this paper, we first derive the fully efficient Bayes predictor by utilizing all the available data. We then estimate the regression and variance component parameters in the model to get an empirical Bayes (EB) predictor and show that the EB predictor is asymptotically optimal. In addition, we employ the jackknife method to obtain an estimator of mean squared prediction error (MSPE) of the EB predictor. Finally, we report the results of a simulation study on the performance of our EB predictor and associated jackknife MSPE estimators. Our results show that the proposed EB predictor can lead to significant gain in efficiency over the previously proposed EB predictor. Copyright (c) 2008 Board of the Foundation of the Scandinavian Journal of Statistics.
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