PubMed Health⌕ Search

Biomedical subjects

Paul E Green

Publications and source records attributed to Paul E Green.

2 recordsLinked to original sources
PubMed2006

The estimated reduction in the odds of loss-of-control type crashes for sport utility vehicles equipped with electronic stability control.

INTRODUCTION: Using data from the NASS General Estimates System (GES), the method of induced exposure was used to assess the effects of electronic stability control (ESC) on loss-of-control type crashes for sport utility vehicles. METHOD: Sport utility vehicles were classified into crash types generally associated with loss of control and crash types most likely not associated with loss of control. Vehicles were then compared as to whether ESC technology was present or absent in the vehicles. A generalized additive model was fit to assess the effects of ESC, driver age, and driver gender on the odds of loss of control. In addition, the effects of ESC on roads that were not dry were compared to effects on roads that were dry. RESULTS: Overall, the estimated percentage reduction in the odds of a loss-of-control crash for sport utility vehicles equipped with ESC was 70.3%. Both genders and all age groups showed reduced odds of loss-of-control crashes, but there was no significant difference between males and females. With respect to driver age, the maximum percentage reduction of 73.6% occurred at age 27. The positive effects of ESC on roads that were not dry were significantly greater than on roads that were dry.

Accidents, Traffic↗
PubMed2003

A Bayesian hierarchical model for categorical data with nonignorable nonresponse.

Log-linear models have been shown to be useful for smoothing contingency tables when categorical outcomes are subject to nonignorable nonresponse. A log-linear model can be fit to an augmented data table that includes an indicator variable designating whether subjects are respondents or nonrespondents. Maximum likelihood estimates calculated from the augmented data table are known to suffer from instability due to boundary solutions. Park and Brown (1994, Journal of the American Statistical Association 89, 44-52) and Park (1998, Biometrics 54, 1579-1590) developed empirical Bayes models that tend to smooth estimates away from the boundary. In those approaches, estimates for nonrespondents were calculated using an EM algorithm by maximizing a posterior distribution. As an extension of their earlier work, we develop a Bayesian hierarchical model that incorporates a log-linear model in the prior specification. In addition, due to uncertainty in the variable selection process associated with just one log-linear model, we simultaneously consider a finite number of models using a stochastic search variable selection (SSVS) procedure due to George and McCulloch (1997, Statistica Sinica 7, 339-373). The integration of the SSVS procedure into a Markov chain Monte Carlo (MCMC) sampler is straightforward, and leads to estimates of cell frequencies for the nonrespondents that are averages resulting from several log-linear models. The methods are demonstrated with a data example involving serum creatinine levels of patients who survived renal transplants. A simulation study is conducted to investigate properties of the model.

Algorithms↗