The clinical utility of a molecular test rises proportional to a favorable regulatory risk/benefit assessment, and clinical utility is the driver of payer coverage decisions. Although a great deal has been written about clinical utility, debates still center on its 'definition.' We argue that the definition (an impact on clinical outcomes) is self-evident, and improved communications should foc...

OBSERVATION Outbreak situations require in vitro diagnostics (IVDs) to identify those who are infected and to track the infectious agent in the population. However, such IVDs are typically not available and must be developed. In addition, the process of IVD development, assessment, and implementation are very time and resource intensive. Recognising the extraordinary public health need for IVDs...

Abstract We present a collection of modeling tools for the analysis of neuronal spike trains as point processes. The conditional intensity can be modeled to depend on variables such as time elapsed since the last spike, time elapsed since the onset of stimulus, etc., and the models can be estimated nonparametrically by penalized likelihood Poisson regression or logistic regression. Model diagno...

Multiple Logistic Regression Just as in OLS regression, logistic models can include more than one predictor. The analysis options are similar to regression. One can choose to select variables, as with a stepwise procedure, or one can enter the predictors simultaneously, or they can be entered in blocks. Variations of the likelihood ratio test can be conducted in which the chi-square test (G) is...

Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are present. Besides, it is hard to judge model adequacy because there are few diagnostics for choosing variable transformations and no true goodness-of-fit test. To overcome these problems, this article ...