Diagnostic Accuracy of a Bayesian Latent Group Analysis for the Detection of Malingering-Related Poor Effort

Ortega A, Labrenz S, Markowitsch HJ, Piefke M (2013)
The Clinical Neuropsychologist 27(6): 1019-1042.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
Abstract / Bemerkung
In the last decade, different statistical techniques have been introduced to improve assessment of malingering-related poor effort. In this context, we have recently shown preliminary evidence that a Bayesian latent group model may help to optimize classification accuracy using a simulation research design. In the present study, we conducted two analyses. Firstly, we evaluated how accurately this Bayesian approach can distinguish between participants answering in an honest way (honest response group) and participants feigning cognitive impairment (experimental malingering group). Secondly, we tested the accuracy of our model in the differentiation between patients who had real cognitive deficits (cognitively impaired group) and participants who belonged to the experimental malingering group. All Bayesian analyses were conducted using the raw scores of a visual recognition forced-choice task (2AFC), the Test of Memory Malingering (TOMM, Trial 2), and the Word Memory Test (WMT, primary effort subtests). The first analysis showed 100% accuracy for the Bayesian model in distinguishing participants of both groups with all effort measures. The second analysis showed outstanding overall accuracy of the Bayesian model when estimates were obtained from the 2AFC and the TOMM raw scores. Diagnostic accuracy of the Bayesian model diminished when using the WMT total raw scores. Despite, overall diagnostic accuracy can still be considered excellent. The most plausible explanation for this decrement is the low performance in verbal recognition and fluency tasks of some patients of the cognitively impaired group. Additionally, the Bayesian model provides individual estimates, p(z(i) |D), of examinees' effort levels. In conclusion, both high classification accuracy levels and Bayesian individual estimates of effort may be very useful for clinicians when assessing for effort in medico-legal settings.
Erscheinungsjahr
Zeitschriftentitel
The Clinical Neuropsychologist
Band
27
Zeitschriftennummer
6
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1019-1042
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Ortega A, Labrenz S, Markowitsch HJ, Piefke M. Diagnostic Accuracy of a Bayesian Latent Group Analysis for the Detection of Malingering-Related Poor Effort. The Clinical Neuropsychologist. 2013;27(6):1019-1042.
Ortega, A., Labrenz, S., Markowitsch, H. J., & Piefke, M. (2013). Diagnostic Accuracy of a Bayesian Latent Group Analysis for the Detection of Malingering-Related Poor Effort. The Clinical Neuropsychologist, 27(6), 1019-1042. doi:10.1080/13854046.2013.806677
Ortega, A., Labrenz, S., Markowitsch, H. J., and Piefke, M. (2013). Diagnostic Accuracy of a Bayesian Latent Group Analysis for the Detection of Malingering-Related Poor Effort. The Clinical Neuropsychologist 27, 1019-1042.
Ortega, A., et al., 2013. Diagnostic Accuracy of a Bayesian Latent Group Analysis for the Detection of Malingering-Related Poor Effort. The Clinical Neuropsychologist, 27(6), p 1019-1042.
A. Ortega, et al., “Diagnostic Accuracy of a Bayesian Latent Group Analysis for the Detection of Malingering-Related Poor Effort”, The Clinical Neuropsychologist, vol. 27, 2013, pp. 1019-1042.
Ortega, A., Labrenz, S., Markowitsch, H.J., Piefke, M.: Diagnostic Accuracy of a Bayesian Latent Group Analysis for the Detection of Malingering-Related Poor Effort. The Clinical Neuropsychologist. 27, 1019-1042 (2013).
Ortega, Alonso, Labrenz, Stephan, Markowitsch, Hans J., and Piefke, Martina. “Diagnostic Accuracy of a Bayesian Latent Group Analysis for the Detection of Malingering-Related Poor Effort”. The Clinical Neuropsychologist 27.6 (2013): 1019-1042.

1 Zitation in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

A Bayesian latent group analysis for detecting poor effort in a sample of cognitively impaired patients.
Ortega A, Piefke M, Markowitsch HJ., J Clin Exp Neuropsychol 36(6), 2014
PMID: 24911397

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