Review of Statistical and Methodological Issues in the Forensic Prediction of Malingering from Validity Tests: Part I: Statistical Issues.

Abstract

Forensic neuropsychological examinations with determination of malingering have tremendous social, legal, and economic consequences. Thousands of studies have been published aimed at developing and validating methods to diagnose malingering in forensic settings, based largely on approximately 50 validity tests, including embedded and stand-alone performance validity tests. This is the first part of a two-part review. Part I explores three statistical issues related to the validation of validity tests as predictors of malingering, including (a) the need to report a complete set of classification accuracy statistics, (b) how to detect and handle collinearity among validity tests, and (c) how to assess the classification accuracy of algorithms for aggregating information from multiple validity tests. In the Part II companion paper, three closely related research methodological issues will be examined. Statistical issues are explored through conceptual analysis, statistical simulations, and through reanalysis of findings from prior validation studies. Findings suggest extant neuropsychological validity tests are collinear and contribute redundant information to the prediction of malingering among forensic examinees. Findings further suggest that existing diagnostic algorithms may miss diagnostic accuracy targets under most realistic conditions. The review makes several recommendations to address these concerns, including (a) reporting of full confusion table statistics with 95% confidence intervals in diagnostic trials, (b) the use of logistic regression, and (c) adoption of the consensus model on the "transparent reporting of multivariate prediction models for individual prognosis or diagnosis" (TRIPOD) in the malingering literature.