Verification bias on sensitivity and specificity measurements in diagnostic medicine: a comparison of some approaches used for correction

Verification bias may occur when the test results of not all subjects are verified by using a gold standard. The correction for this bias can be made using different approaches depending on whether missing gold standard test results are random or not. Some of these approaches with binary test and gold standard results include the correction method by Begg and Greenes, lower and upper limits for diagnostic measurements by Zhou, logistic regression method, multiple imputation method, and neural networks. In this study, all these approaches are compared by employing a real and simulated data under different conditions.     

Simulation-based designs for accelerated life tests

In this paper we present a Bayesian decision theoretic approach to the design of accelerated life tests (ALT). We discuss computational issues regarding the evaluation of expectation and optimization steps in the solution of the decision problem. We illustrate how Monte Carlo methods can be used in preposterior analysis to find optimal designs and how the required computational effort can be avoided by using curve-fitting techniques. In so doing, we adopt the recent Monte-Carlo-based approaches of Muller and Parmigiani (1995. J. Amer. Statist. Assoc. 90, 503–510) and Muller (2000. Bayesian Statistics 6, forthcoming) to develop optimal Bayesian designs. These approaches facilitate the preposterior analysis by replacing it with a sequence of scatter plot smoothing/regression techniques and optimization of the corresponding fitted surfaces. We present our development by considering single and multiple-point fixed, as well as, sequential design problems when the underlying life model is exponential, and illustrate the implementation of our approach with some examples.     

Linear Bayesian Inference for Accelerated Weibull Model

In this paper, we present a Bayesian approach for inference from accelerated life tests when the underlying life model is Weibull. Our approach is based on the General Linear Models framework of West, Harrison and Migon (1985). We discuss inference for the model and show that computable results can be obtained using linear Bayesian methods. We illustrate the usefulness of our approach by applying it to some actual data from accelerated life tests. This revised version was published online in July 2006 with corrections to the Cover Date.     

On the Use of the Concentration Function in Medical Fraud Assessment

We propose a simple, but effective, tool to detect possible anomalies in the services prescribed by a health care provider (HP) compared to his/her colleagues in the same field and environment. Our method is based on the concentration function that is an extension of the Lorenz curve widely used in describing uneven distribution of wealth in a population. The proposed tool provides a graphical illustration of a possible anomalous behavior of the HPs and it can be used as a prescreening device for further investigations of potential medical fraud.