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The article details a sampling scheme which can lead to a reduction in sample size and cost in clinical and epidemiological studies of association between a count outcome and risk factor. We show that inference in two common generalized linear models for count data, Poisson and negative binomial regression, is improved by using a ranked auxiliary covariate, which guides the sampling procedure. This type of sampling has typically been used to improve inference on a population mean. The novelty of the current work is its extension to log-linear models and derivations showing that the sampling technique results in an increase in information as compared to simple random sampling. Specifically, we show that under the proposed sampling strategy the maximum likelihood estimate of the risk factor’s coefficient is improved through an increase in the Fisher’s information. A simulation study is performed to compare the mean squared error, bias, variance, and power of the sampling routine with simple random sampling under various data-generating scenarios. We also illustrate the merits of the sampling scheme on a real data set from a clinical setting of males with chronic obstructive pulmonary disease. Empirical results from the simulation study and data analysis coincide with the theoretical derivations, suggesting that a significant reduction in sample size, and hence study cost, can be realized while achieving the same precision as a simple random sample. [ABSTRACT FROM AUTHOR]

The validity of statistical inference depends on proper randomization methods. However, even with proper randomization, we can have imbalanced with respect to important characteristics. In this paper, we introduce a method based on ranked auxiliary variables for treatment allocation in crossover designs using Latin squares models. We evaluate the improvement of the efficiency in treatment comparisons using the proposed method. Our simulation study reveals that our proposed method provides a more powerful test compared to simple randomization with the same sample size. The proposed method is illustrated by conducting an experiment to compare two different concentrations of titanium dioxide nanofiber (TDNF) on rats for the purpose of comparing weight gain. [ABSTRACT FROM AUTHOR]

Regression procedures are not only hindered by largepand smalln, but can also suffer in cases when outliers are present or the data generating mechanisms are heavy tailed. Since the penalized estimates like the least absolute shrinkage and selection operator (LASSO) are equipped to deal with the largepsmallnby encouraging sparsity, we combine a LASSO type penalty with the absolute deviation loss function, instead of the standard least squares loss, to handle the presence of outliers and heavy tails. The model is cast in a Bayesian setting and a Gibbs sampler is derived to efficiently sample from the posterior distribution. We compare our method to existing methods in a simulation study as well as on a prostate cancer data set and a base deficit data set from trauma patients. [ABSTRACT FROM PUBLISHER]

Purpose: Using a longitudinal sample of freestanding Medicare-certified hospices in the 50 US states and the District of Columbia, this study sought to explore the factors associated with volunteer demand and describe how volunteer use has changed from 2000 to 2010. Results: A temporal decline in the extent of use of volunteers in freestanding hospices was observed over the study period. Findings indicated that both organizational and environmental factors influence the use of volunteers in US freestanding hospices. Conclusion: Given the importance of volunteers, both in the preservation of hospices’ philanthropic traditions and in reducing health care expenditure at the end of life, research is needed to further evaluate the factors associated with this decline. Emphasis should be placed on improving the retention of the existing hospice volunteer workforce. [ABSTRACT FROM AUTHOR]