Highland, AR, United States

Lyon College

www.lyon.edu
Highland, AR, United States

Lyon College is an independent, residential, co-educational, undergraduate liberal arts college affiliated with the Presbyterian Church . Founded in 1872, it is the oldest independent college in Arkansas. Wikipedia.

SEARCH FILTERS
Time filter
Source Type

News Article | April 17, 2017
Site: www.prweb.com

Cleophas T.J.,Lyon College
Journal of Pharmaceutical Sciences and Research | Year: 2012

Background: Traditional statistical tests are unable to handle a large number of variables. The simplest method to reduce large numbers of variables is the use of add-up scores. But add-up scores do not account for the relative importance of the separate variables, their interactions and differences in units. Principal components analysis and partial least square analysis account all of that, but are virtually unused in clinical trials. Objective: To assess the performance of either of the two methods. Methods: A simulated example of 250 patients' gene expression data as predictor and drug efficacy scores as outcome will be used. For principal components analysis SPSS' s Data Dimension Reduction module was used, for partial least square analysis R Partial Least Squares, a free statistics and forecasting software was used. Results: Of 27 variables three novel predictor variables were constructed. With principal components analysis the 3 were very significant predictors of the add-up outcome score with t-values of 10.2, 21.6, and 6.7 (p<0.000, p<0.000, p<0.000). Partial least squares included the outcome variables in its program, and was also able to predict the outcome variables although at a lower level of significance with t-values of 6.8, 16.2, and 3.5 (p<0.000, p<0.000, p<0.001). Traditional multiple linear regression with the novel predictors in the form of add-up scores as independent variables produced a consistent further reduction of significance with t-values of 3.4, 11.2 and 2.4 (p<0.002, p<0.001, p<0.02). Conclusions: 1. Principal components analysis and partial least squares can handle many more variables than the standard covariance methods like MANOVA and MANCOVA can, and is more sensitive than add-up scores. 2.The methods account the relative importance of the separate variables, their interactions and differences in units. They are also very flexible, to the extent that manifest variables can be applied twice, first in the form of clusters for prediction and second unclusteredly as manifest outcome variables. Partial least squares method is parsimonious to principal components analysis, because it can include outcome variables in the model.

Cleophas T.J.,Lyon College
American Journal of Therapeutics | Year: 2016

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method. © 2013 Wolters Kluwer Health, Inc.

Cleophas T.J.,Lyon College
American Journal of Therapeutics | Year: 2016

Traditionally, nonlinear relationships like the smooth shapes of airplanes, boats, and motor cars were constructed from scale models using stretched thin wooden strips, otherwise called splines. In the past decades, mechanical spline methods have been replaced with their mathematical counterparts. The objective of the study was to study whether spline modeling can adequately assess the relationships between exposure and outcome variables in a clinical trial and also to study whether it can detect patterns in a trial that are relevant but go unobserved with simpler regression models. A clinical trial assessing the effect of quantity of care on quality of care was used as an example. Spline curves consistent of 4 or 5 cubic functions were applied. SPSS statistical software was used for analysis. The spline curves of our data outperformed the traditional curves because (1) unlike the traditional curves, they did not miss the top quality of care given in either subgroup, (2) unlike the traditional curves, they, rightly, did not produce sinusoidal patterns, and (3) unlike the traditional curves, they provided a virtually 100% match of the original values. We conclude that (1) spline modeling can adequately assess the relationships between exposure and outcome variables in a clinical trial; (2) spline modeling can detect patterns in a trial that are relevant but may go unobserved with simpler regression models; (3) in clinical research, spline modeling has great potential given the presence of many nonlinear effects in this field of research and given its sophisticated mathematical refinement to fit any nonlinear effect in the mostly accurate way; and (4) spline modeling should enable to improve making predictions from clinical research for the benefit of health decisions and health care. We hope that this brief introduction to spline modeling will stimulate clinical investigators to start using this wonderful method. © 2013 Wolters Kluwer Health, Inc.

Coleman M.T.,FutureFuel Chemical Company | Leblanc G.,Lyon College
Organic Process Research and Development | Year: 2010

The effectiveness of diethoxymethane (DEM) as a solvent for O-alkylation of a variety of phenols under phase transfer conditions has been examined and evaluated. The reaction between 4-methoxy phenol and benzyl chloride was selected to compare reaction rates in various solvents and the efficiency of various PTCs. This reaction was further studied to develop a commercially amenable process complete with recycle streams and efficient product isolation. DEM is a good solvent for these types of phase transfer-catalyzed reactions and can be considered as an alternative solvent for dichloromethane and toluene. © 2010 American Chemical Society.

Lyon College | Date: 2016-08-22

Binders; Notebook covers; Notebooks; Pens. Cups; Glass beverageware; Glass mugs; Mugs; Tumblers for use as drinking glasses; Water bottles sold empty; Coffee mugs; Cups and mugs; Drinking glasses, namely, tumblers; Glass mugs.

Lyon College | Date: 2016-08-22

Decals; Folders; Notebook covers; Notebooks.

Lyon College | Date: 2016-07-20

Hats; Shirts; Shirts and short-sleeved shirts; Shorts; Sweat pants; Athletic shirts; Body shirts; Collared shirts; Hooded sweat shirts; Knit shirts; Long-sleeved shirts; Open-necked shirts; Short-sleeve shirts; Short-sleeved or long-sleeved t-shirts; Sport shirts; Sports shirts; Sports shirts with short sleeves; Sweat shirts; T-shirts; Tee shirts; Tee-shirts.