American Academy of Clinical Neuropsychology Consensus Conference Statement on the neuropsychological assessment of effort, response bias, and malingering.

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Abstract

During the past two decades clinical and research efforts have led to increasingly sophisticated and effective methods and instruments designed to detect exaggeration or fabrication of neuropsychological dysfunction, as well as somatic and psychological symptom complaints. A vast literature based on relevant research has emerged and substantial portions of professional meetings attended by clinical neuropsychologists have addressed topics related to malingering (Sweet, King, Malina, Bergman, & Simmons, 2002). Yet, despite these extensive activities, understanding the need for methods of detecting problematic effort and response bias and addressing the presence or absence of malingering has proven challenging for practitioners. A consensus conference, comprised of national and international experts in clinical neuropsychology, was held at the 2008 Annual Meeting of the American Academy of Clinical Neuropsychology (AACN) for the purposes of refinement of critical issues in this area. This consensus statement documents the current state of knowledge and recommendations of expert clinical neuropsychologists and is intended to assist clinicians and researchers with regard to the neuropsychological assessment of effort, response bias, and malingering.

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Applied Logistic Regression Analysis

Scott Menard (2002)

The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered. The book includes detailed discussions of goodness of fit, indices of predictive efficiency, and standardized logistic regression coefficients, and examples using SAS and SPSS are included. More detailed consideration of grouped as opposed to case-wise data throughout the book Updated discussion of the properties and appropriate use of goodness of fit measures, R-square analogues, and indices of predictive efficiency Discussion of the misuse of odds ratios to represent risk ratios, and of over-dispersion and under-dispersion for grouped data Updated coverage of unordered and ordered polytomous logistic regression models.

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Assessing the generalizability of prognostic information.

A Justice, K. Covinsky, J A Berlin (1999)

Physicians are often asked to make prognostic assessments but often worry that their assessments will prove inaccurate. Prognostic systems were developed to enhance the accuracy of such assessments. This paper describes an approach for evaluating prognostic systems based on the accuracy (calibration and discrimination) and generalizability (reproducibility and transportability) of the system's predictions. Reproducibility is the ability to produce accurate predictions among patients not included in the development of the system but from the same population. Transportability is the ability to produce accurate predictions among patients drawn from a different but plausibly related population. On the basis of the observation that the generalizability of a prognostic system is commonly limited to a single historical period, geographic location, methodologic approach, disease spectrum, or follow-up interval, we describe a working hierarchy of the cumulative generalizability of prognostic systems. This approach is illustrated in a structured review of the Dukes and Jass staging systems for colon and rectal cancer and applied to a young man with colon cancer. Because it treats the development of the system as a "black box" and evaluates only the performance of the predictions, the approach can be applied to any system that generates predicted probabilities. Although the Dukes and Jass staging systems are discrete, the approach can also be applied to systems that generate continuous predictions and, with some modification, to systems that predict over multiple time periods. Like any scientific hypothesis, the generalizability of a prognostic system is established by being tested and being found accurate across increasingly diverse settings. The more numerous and diverse the settings in which the system is tested and found accurate, the more likely it will generalize to an untested setting.