Showing papers by "Ran Wu published in 2014"

Ethyl glucuronide and ethyl sulfate assays in clinical trials, interpretation, and limitations: results of a dose ranging alcohol challenge study and 2 clinical trials.

Abstract: Background The ethanol metabolites, ethyl glucuronide (EtG) and ethyl sulfate (EtS) are biomarkers of recent alcohol consumption that provide objective measures of abstinence. Our goals are to better understand the impact of cutoff concentration on test interpretation, the need for measuring both metabolites, and how best to integrate test results with self-reports in clinical trials.

Smoking Cessation Is Followed by Increases in Serum Bilirubin, an Endogenous Antioxidant Associated With Lower Risk of Lung Cancer and Cardiovascular Disease

Abstract: Introduction: Lower concentrations of serum bilirubin, an endogenous antioxidant, have been associated with risk of many smoking-related diseases, including lung cancer and cardiovascular disease, and current smokers are reported to have lower bilirubin levels than nonsmokers and past smokers. This study evaluates the effects of smoking cessation on bilirubin levels.

Predictors of abstinence from heavy drinking during treatment in COMBINE and external validation in PREDICT.

Abstract: BACKGROUND: The goal of the current study was to use tree-based methods (Zhang and Singer, 2010, Recursive Partitioning and Applications, 2nd ed Springer, New York) to identify predictors of abstinence from heavy drinking in COMBINE (Anton et al JAMA 2006; 295:2003), the largest study of pharmacotherapy for alcoholism in the United States to date, and to validate these results in PREDICT (Mann et al Addict Biol 2012; 18:937), a parallel study conducted in Germany METHODS: We compared a classification tree constructed according to purely statistical criteria to a tree constructed according to a combination of statistical criteria and clinical considerations for prediction of no heavy drinking during treatment in COMBINE We considered over 100 baseline predictors The tree approach was compared to logistic regression The trees and a deterministic forest identified the most important predictors of no heavy drinking for direct testing in PREDICT RESULTS: The tree built using both clinical and statistical considerations consisted of 4 splits based on consecutive days of abstinence (CDA) prior to randomization, age, family history of alcoholism, and confidence to resist drinking in response to withdrawal and urges The tree based on statistical considerations with 4 splits also split on CDA and age but also on gamma-glutamyl transferase level and drinking goal Deterministic forest identified CDA, age, and drinking goal as the most important predictors Backward elimination logistic regression among the top 18 predictors identified in the deterministic forest analyses identified only age and CDA as significant main effects Longer CDA and goal of complete abstinence were associated with better outcomes in both data sets CONCLUSIONS: The most reliable predictors of abstinence from heavy drinking were CDA and drinking goal Trees provide binary decision rules and straightforward graphical representations for identification of subgroups based on response and may be easier to implement in clinical settings Language: en