University of Memphis

About: University of Memphis is a education organization based out in Memphis, Tennessee, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 7710 authors who have published 20082 publications receiving 611618 citations. The organization is also known as: U of M.

The Influences of Friends' and Parental Smoking on Adolescent Smoking Behavior: The Effects of Time and Prior Smoking1

Abstract: This research examines the relative importance of parental and friends' influences on adolescents' smoking behavior and changes in the effects of social influences during adolescence. Data were collected at 4 times from 7th to 9th grades. Random-effects ordinal regression models were employed to predict the repeated classification of adolescent smoking status using time effects, prior smoking status, friends' smoking, and parental smoking. In general, the effects of friends' smoking are stronger than those of parental smoking, and these differences increase over time. In addition, friends' smoking has greater effects on nonsmokers than smokers. Separate models for males and females disclose some gender differences. In particular, the effects of friends' smoking are stronger for females than for males, and the increasing trend of friends' influences is more noticeable for females than for males. Models for 4 ethnic groups also suggest differential susceptibility to social influences in different cultures.

Hybrid Empirical Ground-Motion Prediction Equations for Eastern North America Using NGA Models and Updated Seismological Parameters

Abstract: In the field of earthquake engineering, ground-motion prediction models are frequently used to estimate the peak ground acceleration (PGA) and the pseudos- pectral acceleration (PSA). In regions of the world where ground-motion recordings are plentiful, such as western North America (WNA), the ground-motion prediction equations are obtained using empirical methods. In other regions, such as eastern North America (ENA), with insufficient ground-motion data, alternative methods must be used to develop ground-motion prediction equations (GMPEs). The hybrid empiri- cal method is one such method used to develop ground-motion prediction equations in areas with sparse ground motions. This method employs the stochastic simulation method to adjust empirical GMPEs developed for a region with abundant strong- motion recordings in order to estimate strong-motion parameters in a region with a sparse database. The adjustments take into account differences in the earthquake source, wave propagation, and site-response characteristics between the two regions. In this study, a hybrid empirical method is used to develop a new GMPE for ENA, using five new ground-motion prediction models developed by the Pacific Earthquake Engineering Research Center (PEER) for WNA. A new ENA GMPE is derived for a magnitude range of 5 to 8 and closest distances to the fault rupture up to 1000 km. Ground-motion prediction equations are developed for the response spectra (pseudoacceleration, 5% damped) and the PGA for hard-rock sites in ENA. The resulting ground-motion prediction model developed in this study is compared with two ENA ground-motion models used in the 2008 national seismic hazard maps as well as with available observed data for ENA.

Tests for no treatment effect in randomized clinical trials

Abstract: SUMMARY We propose a test of the null hypothesis of no treatment effect in a randomized clinical trial that is based on the randomization distribution of residuals. These residuals result from regressing the response on covariates, but not treatment. In contrast to model-based score tests, this procedure maintains nominal size when the model is misspecified, and, in particular, when relevant covariates are omitted from the regression. The efficiency of the procedure is evaluated for regressions with some, but not all, required covariates. For many generalized linear models and survival models, conventional model-based score tests are shown to have supranominal size when relevant covariates are omitted, but logistic regression and the proportional hazards model are robust.