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Full-Text Articles in Mathematics
Results Of Soy-Based Meal Replacement Formula On Weight, Anthropometry, Serum Lipids & Blood Pressure During A 40-Week Clinical Weight Loss Trial, Kevin R. Fontaine, Dongyan Yang, Gary L. Gadbury, Stanley Heshka, Linda G. Schwartz, Radha Murugesan, Jennifer L. Kraker, Moonseong Heo, Steven B. Heymsfield, David B. Allison
Results Of Soy-Based Meal Replacement Formula On Weight, Anthropometry, Serum Lipids & Blood Pressure During A 40-Week Clinical Weight Loss Trial, Kevin R. Fontaine, Dongyan Yang, Gary L. Gadbury, Stanley Heshka, Linda G. Schwartz, Radha Murugesan, Jennifer L. Kraker, Moonseong Heo, Steven B. Heymsfield, David B. Allison
Mathematics and Statistics Faculty Research & Creative Works
Background: to evaluate the intermediate-term health outcomes associated with a soy-Based meal replacement, and to compare the weight loss efficacy of two distinct patterns of caloric restriction. Methods: Ninety overweight/obese (28 < BMI ≤ 41 kg/m2) adults received a single session of dietary counseling and were randomized to either 12 weeks at 1200 kcal/day, 16 weeks at 1500 kcal/d and 12 weeks at 1800 kcal/d (i.e., the 12/15/18 diet group), or 28 weeks at 1500 kcal/d and 12 weeks at 1800 kcal/d (i.e., the 15/18 diet group). Weight, body fat, waist circumference, blood pressure and serum lipid concentrations were measured at 4-week intervals throughout the 40-week trial. Results: Subjects in both treatments showed statistically significant improvements in outcomes. a regression model for weight change suggests that subjects with larger baseline weights tended to lose more weight and subjects in the 12/15/18 group tended to experience, on average, an additional 0.9 kg of weight loss compared with subjects in the 15/18 group. Conclusion: Both treatments using the soy-Based meal replacement program were associated with significant and comparable weight loss and improvements on selected health variables.
Randomization Tests For Small Samples: An Application For Genetic Expression Data, Gary L. Gadbury, Grier P. Page, Moonseong Heo, John D. Mountz, David B. Allison
Randomization Tests For Small Samples: An Application For Genetic Expression Data, Gary L. Gadbury, Grier P. Page, Moonseong Heo, John D. Mountz, David B. Allison
Mathematics and Statistics Faculty Research & Creative Works
An advantage of randomization tests for small samples is that an exact P-value can be computed under an additive model. a disadvantage with very small sample sizes is that the resulting discrete distribution for P-values can make it mathematically impossible for a P-value to attain a particular degree of significance. We investigate a distribution of P-values that arises when several thousand randomization tests are conducted simultaneously using small samples, a situation that arises with microarray gene expression data. We show that the distribution yields valuable information regarding groups of genes that are differentially expressed between two groups: A treatment group …
Modern Statistical Methods For Handling Missing Repeated Measurements In Obesity Trial Data: Beyond Locf, Gary L. Gadbury, C. S. Coffey, D. B. Allison
Modern Statistical Methods For Handling Missing Repeated Measurements In Obesity Trial Data: Beyond Locf, Gary L. Gadbury, C. S. Coffey, D. B. Allison
Mathematics and Statistics Faculty Research & Creative Works
This paper brings together some modern statistical methods to address the problem of missing data in obesity trials with repeated measurements. Such missing data occur when subjects miss one or more follow-up visits or drop out early from an obesity trial. a common approach to dealing with missing data because of dropout is 'last observation carried forward' (LOCF). This method, although intuitively appealing, requires restrictive assumptions to produce valid statistical conclusions. We review the need for obesity trials, the assumptions that must be made regarding missing data in such trials, and some modern statistical methods for analyzing data containing missing …
Cause-Effect Relationships In Analytical Surveys: An Illustration Of Statistical Issues, Gary L. Gadbury, Hans T. Schreuder
Cause-Effect Relationships In Analytical Surveys: An Illustration Of Statistical Issues, Gary L. Gadbury, Hans T. Schreuder
Mathematics and Statistics Faculty Research & Creative Works
Establishing cause-effect is critical in the field of natural resources where one may want to know the impact of management practices, wildfires, drought, etc. on water quality and quantity, wildlife, growth and survival of desirable trees for timber production, etc. Yet, key obstacles exist when trying to establish cause-effect in such contexts. Issues involved with identifying a causal hypothesis, and conditions needed to estimate a causal effect or to establish cause-effect are considered. Ideally one conducts an experiment and follows with a survey, or vice versa. in an experiment, the population of inference may be quite limited and in surveys, …
An Oscillation Theorem For Discrete Eigenvalue Problems, Martin Bohner, Ondřej Došlý, Werner Kratz
An Oscillation Theorem For Discrete Eigenvalue Problems, Martin Bohner, Ondřej Došlý, Werner Kratz
Mathematics and Statistics Faculty Research & Creative Works
In this paper we consider problems that consist of symplectic difference systems depending on an eigenvalue parameter, together with self-adjoint boundary conditions. Such symplectic difference systems contain as important cases linear Hamiltonian difference systems and also Sturm-Liouville difference equations of second and of higher order. The main result of this paper is an oscillation theorem that relates the number of eigenvalues to the number of generalized zeros of solutions.