Forensic facial examiners versus super-recognizers: Evaluating behavior beyond accuracy.

We evaluated the detailed, behavioral properties of face matching performance in two specialist groups: forensic facial examiners and super-recognizers. Both groups compare faces to determine identity with high accuracy and outperform the general population. Typically, facial examiners are highly trained; super-recognizers rely on natural ability.

Estimating the AUC with a Graphical Lasso Method for High-dimensional Biomarkers with LOD.

This manuscript estimates the area under the receiver operating characteristic curve (AUC) of combined biomarkers in a high-dimensional setting. We propose a penalization approach to the inference of precision matrices in the presence of the limit of detection. A new version of expectation-maximization algorithm is then proposed for the penalized likelihood, with the use of numerical integration and the graphical lasso method.

Unified Least Squares Methods for the Evaluation of Diagnostic Tests With the Gold Standard.

The article proposes a unified least squares method to estimate the receiver operating characteristic (ROC) parameters for continuous and ordinal diagnostic tests, such as cancer biomarkers. The method is based on a linear model framework using the empirically estimated sensitivities and specificities as input "data." It gives consistent estimates for regression and accuracy parameters when the underlying continuous test results are normally distributed after some monotonic transformation.

Least squares regression methods for clustered ROC data with discrete covariates.

The receiver operating characteristic (ROC) curve is a popular tool to evaluate and compare the accuracy of diagnostic tests to distinguish the diseased group from the nondiseased group when test results from tests are continuous or ordinal. A complicated data setting occurs when multiple tests are measured on abnormal and normal locations from the same subject and the measurements are clustered within the subject. Although least squares regression methods can be used for the estimation of ROC curve from correlated data, how to develop the least squares methods to estimate the ROC curve from the clustered data has not been studied.

Optimal sampling ratios in comparative diagnostic trials.

A subjective sampling ratio between the case and the control groups is not always an efficient choice to maximize the power or to minimize the total required sample size in comparative diagnostic trials.We derive explicit expressions for an optimal sampling ratio based on a common variance structure shared by several existing summary statistics of the receiver operating characteristic curve. We propose a two-stage procedure to estimate adaptively the optimal ratio without pilot data.

WHICH CRIMINOGENIC NEED CHANGES ARE MOST IMPORTANT IN PROMOTING DESISTANCE FROM CRIME AND SUBSTANCE USE?

Andrews and Bonta identified the following criminogenic needs as important to reducing offending: substance use, antisocial cognition, antisocial associates, family and marital relations, employment, and leisure and recreational activities. This study examines dynamic criminogenic need changes across a 12-month period and identifies which need changes are the best predictors of criminal offending and illicit drug use among a sample of drug-involved probationers who participated in an intervention ( = 251). Probationers had significant changes in several need areas, and treatment participation moderated some changes.

A statistical method for synthesizing meta-analyses.

Multiple meta-analyses may use similar search criteria and focus on the same topic of interest, but they may yield different or sometimes discordant results. The lack of statistical methods for synthesizing these findings makes it challenging to properly interpret the results from multiple meta-analyses, especially when their results are conflicting. In this paper, we first introduce a method to synthesize the meta-analytic results when multiple meta-analyses use the same type of summary effect estimates.

Combining markers with and without the limit of detection.

In this paper, we consider the combination of markers with and without the limit of detection (LOD). LOD is often encountered when measuring proteomic markers. Because of the limited detecting ability of an equipment or instrument, it is difficult to measure markers at a relatively low level.

An additive selection of markers to improve diagnostic accuracy based on a discriminatory measure.

Rationale And Objectives: The estimation of the area under the receiver operating characteristic (ROC) curve (AUC) often relies on the assumption that the truly positive population tends to have higher marker results than the truly negative population. The authors propose a discriminatory measure to relax such an assumption and apply the measure to identify the appropriate set of markers for combination.

Materials And Methods: The proposed measure is based on the maximum of the AUC and 1-AUC.

A semiparametric separation curve approach for comparing correlated ROC data from multiple markers.

In this article we propose a separation curve method to identify the range of false positive rates for which two ROC curves differ or one ROC curve is superior to the other. Our method is based on a general multivariate ROC curve model, including interaction terms between discrete covariates and false positive rates. It is applicable with most existing ROC curve models.

Nonparametric ROC summary statistics for correlated diagnostic marker data.

We propose efficient nonparametric statistics to compare medical imaging modalities in multi-reader multi-test data and to compare markers in longitudinal ROC data. The proposed methods are based on the weighted area under the ROC curve, which includes the area under the curve and the partial area under the curve as special cases. The methods maximize the local power for detecting the difference between imaging modalities.

Homogeneity tests of clustered diagnostic markers with applications to the BioCycle Study.

Diagnostic trials often require the use of a homogeneity test among several markers. Such a test may be necessary to determine the power both during the design phase and in the initial analysis stage. However, no formal method is available for the power and sample size calculation when the number of markers is greater than two and marker measurements are clustered in subjects.

A random-sum Wilcoxon statistic and its application to analysis of ROC and LROC data.

The Wilcoxon-Mann-Whitney statistic is commonly used for a distribution-free comparison of two groups. One requirement for its use is that the sample sizes of the two groups are fixed. This is violated in some of the applications such as medical imaging studies and diagnostic marker studies; in the former, the violation occurs since the number of correctly localized abnormal images is random, while in the latter the violation is due to some subjects not having observable measurements.

Sample size recalculation in sequential diagnostic trials.

Before a comparative diagnostic trial is carried out, maximum sample sizes for the diseased group and the nondiseased group need to be obtained to achieve a nominal power to detect a meaningful difference in diagnostic accuracy. Sample size calculation depends on the variance of the statistic of interest, which is the difference between receiver operating characteristic summary measures of 2 medical diagnostic tests. To obtain an appropriate value for the variance, one often has to assume an arbitrary parametric model and the associated parameter values for the 2 groups of subjects under 2 tests to be compared.