Hospital context in surgical site infection following colorectal surgery: a multi-level logistic regression analysis
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In this paper, we compare logistic regression and 2 other classification methods in predicting hypertension given the genotype information. We use logistic regression analysis in the first step to detect significant single-nucleotide polymorphisms (SNPs). In the second step, we use the significant SNPs with logistic regression, support vector machines (SVMs), and a newly developed permanental classification method for prediction purposes. We also detect rare variants and investigate their impact on prediction. Our results show that SVMs and permanental classification both outperform logistic regression, and they are comparable in predicting hypertension status.
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Summary and Conclusion An analysis of the literature and our own experience with 180 patients strongly support the preoperative employment of intravenous pyelography in colorectal surgery.
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Multi-level structures are common in social work administration research, where individuals are hierarchically nested within organizations. Hierarchical linear modeling (HLM) is a statistical method developed to address issues associated with the unique nature of multilevel data. We introduce the basic notion of HLM, highlighting the need for using this technique in multilevel data. We also present analytical procedures, illustrating the major issues involved in the application of HLM to social work administration research. Currently, there has been an increasing interest in promoting a multilevel approach in social work administration research. To comprehend and benefit from the results of research dealing with the multilevel approach, administrators as well as researchers are expected to understand the basic logic of HLM. This paper delivers sufficient practical knowledge for research consumers to appreciate the results of research that has employed HLM, especially for those consumers who are not familiar with advanced statistics.
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