A Bayesian Latent Group Analysis for Detecting Poor Effort in the Assessment of Malingering

Ortega A, Wagenmakers E-J, Lee MD, Markowitsch HJ, Piefke M (2012)
Archives of Clinical Neuropsychology 27(4): 453-465.

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Abstract
Despite their theoretical appeal, Bayesian methods for the assessment of poor effort and malingering are still rarely used in neuropsychological research and clinical diagnosis. In this article, we outline a novel and easy-to-use Bayesian latent group analysis of malingering whose goal is to identify participants displaying poor effort when tested. Our Bayesian approach also quantifies the confidence with which each participant is classified and estimates the base rates of malingering from the observed data. We implement our Bayesian approach and compare its utility in effort assessment to that of the classic below-chance criterion of symptom validity testing (SVT). In two experiments, we evaluate the accuracy of both a Bayesian latent group analysis and the below-chance criterion of SVT in recovering the membership of participants assigned to the malingering group. Experiment 1 uses a simulation research design, whereas Experiment 2 involves the differentiation of patients with a history of stroke from coached malingerers. In both experiments, sensitivity levels are high for the Bayesian method, but low for the below-chance criterion of SVT. Additionally, the Bayesian approach proves to be resistant to possible effects of coaching. We conclude that Bayesian latent group methods complement existing methods in making more informed choices about malingering.
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Ortega A, Wagenmakers E-J, Lee MD, Markowitsch HJ, Piefke M. A Bayesian Latent Group Analysis for Detecting Poor Effort in the Assessment of Malingering. Archives of Clinical Neuropsychology. 2012;27(4):453-465.
Ortega, A., Wagenmakers, E. - J., Lee, M. D., Markowitsch, H. J., & Piefke, M. (2012). A Bayesian Latent Group Analysis for Detecting Poor Effort in the Assessment of Malingering. Archives of Clinical Neuropsychology, 27(4), 453-465.
Ortega, A., Wagenmakers, E. - J., Lee, M. D., Markowitsch, H. J., and Piefke, M. (2012). A Bayesian Latent Group Analysis for Detecting Poor Effort in the Assessment of Malingering. Archives of Clinical Neuropsychology 27, 453-465.
Ortega, A., et al., 2012. A Bayesian Latent Group Analysis for Detecting Poor Effort in the Assessment of Malingering. Archives of Clinical Neuropsychology, 27(4), p 453-465.
A. Ortega, et al., “A Bayesian Latent Group Analysis for Detecting Poor Effort in the Assessment of Malingering”, Archives of Clinical Neuropsychology, vol. 27, 2012, pp. 453-465.
Ortega, A., Wagenmakers, E.-J., Lee, M.D., Markowitsch, H.J., Piefke, M.: A Bayesian Latent Group Analysis for Detecting Poor Effort in the Assessment of Malingering. Archives of Clinical Neuropsychology. 27, 453-465 (2012).
Ortega, Alonso, Wagenmakers, Eric-Jan, Lee, Michael D., Markowitsch, Hans J., and Piefke, Martina. “A Bayesian Latent Group Analysis for Detecting Poor Effort in the Assessment of Malingering”. Archives of Clinical Neuropsychology 27.4 (2012): 453-465.
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5 Citations in Europe PMC

Data provided by 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
Using the yes/no recognition response pattern to detect memory malingering.
Schindler S, Kissler J, Kuhl KP, Hellweg R, Bengner T., BMC Psychol 1(1), 2013
PMID: 25566364
Memory and self-neuroscientific landscapes.
Markowitsch HJ., ISRN Neurosci 2013(), 2013
PMID: 24967303
Diagnostic accuracy of a bayesian latent group analysis for the detection of malingering-related poor effort.
Ortega A, Labrenz S, Markowitsch HJ, Piefke M., Clin Neuropsychol 27(6), 2013
PMID: 23767801

39 References

Data provided by Europe PubMed Central.

Differential remoteness and emotional tone modulate the neural correlates of autobiographical memory.
Piefke M, Weiss PH, Zilles K, Markowitsch HJ, Fink GR., Brain 126(Pt 3), 2003
PMID: 12566286

AUTHOR UNKNOWN, BAYESIAN ANALYSIS 1(), 2006

AUTHOR UNKNOWN, Behav Sci Law 16(), 1998
A comparison of forensic and nonforensic malingerers: a prototypical analysis of explanatory models.
Rogers R, Salekin RT, Sewell KW, Goldstein A, Leonard K., Law Hum Behav 22(4), 1998
PMID: 9711139

AUTHOR UNKNOWN, Law Hum Behav 18(), 1994
Analysis of Parkinson disease patients from Portugal for mutations in SNCA, PRKN, PINK1 and LRRK2.
Bras J, Guerreiro R, Ribeiro M, Morgadinho A, Januario C, Dias M, Calado A, Semedo C, Oliveira C, Hardy J, Singleton A., BMC Neurol 8(), 2008
PMID: 18211709
A survey of model evaluation approaches with a tutorial on hierarchical bayesian methods.
Shiffrin RM, Lee MD, Kim W, Wagenmakers EJ., Cogn Sci 32(8), 2008
PMID: 21585453
Victoria Symptom Validity Test scores of patients with profound memory impairment: nonlitigants case studies.
Slick DJ, Tan JE, Strauss E, Mateer CA, Harnadek M, Sherman EM., Clin Neuropsychol 17(3), 2003
PMID: 14704889
Bayesian statistical methods for genetic association studies.
Stephens M, Balding DJ., Nat. Rev. Genet. 10(10), 2009
PMID: 19763151
The effects of coaching on the sensitivity and specificity of malingering measures.
Suhr JA, Gunstad J., Arch Clin Neuropsychol 15(5), 2000
PMID: 14590217
Effort indicators within the California Verbal Learning Test-II (CVLT-II).
Wolfe PL, Millis SR, Hanks R, Fichtenberg N, Larrabee GJ, Sweet JJ., Clin Neuropsychol 24(1), 2010
PMID: 19750408
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