Assessment of response bias in mild head injury: beyond malingering tests.

The evaluation of response bias and malingering in the cases of mild head injury should not rely on a single test. Initial injury severity, typical neuropsychological test performance patterns, preexisting emotional stress or chronic social difficulties, history of previous neurological or psychiatric disorder, other system injuries sustained in the accident, preinjury alcohol abuse, and a propensity to attribute benign cognitive and somatic symptoms to a brain injury must be considered along with performances on specific measures of response bias. This article reviews empirically-supported tests and indices. Use of the likelihood ratio and other statistical indicators of diagnostic efficiency are demonstrated. Bayesian model averaging as a statistical technique to derive optimal prediction models is performed with a clinical data set.

Bayesian information criterion for censored survival models.

We investigate the Bayesian Information Criterion (BIC) for variable selection in models for censored survival data. Kass and Wasserman (1995, Journal of the American Statistical Association 90, 928-934) showed that BIC provides a close approximation to the Bayes factor when a unit-information prior on the parameter space is used. We propose a revision of the penalty term in BIC so that it is defined in terms of the number of uncensored events instead of the number of observations. For a simple censored data model, this revision results in a better approximation to the exact Bayes factor based on a conjugate unit-information prior. In the Cox proportional hazards regression model, we propose defining BIC in terms of the maximized partial likelihood. Using the number of deaths rather than the number of individuals in the BIC penalty term corresponds to a more realistic prior on the parameter space and is shown to improve predictive performance for assessing stroke risk in the Cardiovascular Health Study.