title: A Bayesian latent group analysis for detecting poor effort in a sample of cognitively impaired patients.
abstract: Using a Bayesian latent group analysis in a simulation design, we recently showed a high diagnostic accuracy when assessing effort in the context of malingered memory deficits. We here further evaluate our Bayesian model in a sample of cognitively impaired patients. The main analysis showed both high sensitivity and specificity, thus corroborating a high diagnostic accuracy of the model. Additional analysis showed variations on effort estimates after changes in malingering base rates. Variations affected sensitivity, but not specificity, which is in line with typical findings in malingering research. These data suggest that Bayesian analyses may complement and improve existing effort measures.
authors.affiliation: a Physiological Psychology , Bielefeld University , Bielefeld , Germany.
authors.forename: Alonso
authors.initials: A
authors.lastname: Ortega
authors.forename: Martina
authors.initials: M
authors.lastname: Piefke
authors.forename: Hans J
authors.initials: HJ
authors.lastname: Markowitsch
mesh_terms: D001499:Bayes Theorem; D001930:Brain Injuries / Q000150:complications; D002755:Choice Behavior / Q000502:physiology; D003072:Cognition Disorders / Q000150:complications* / Q000175:diagnosis* / Q000209:etiology; D005260:Female; D006801:Humans; D008297:Male; D008306:Malingering / Q000523:psychology; D009103:Multiple Sclerosis / Q000150:complications; D009483:Neuropsychological Tests; D010364:Pattern Recognition, Visual; D010775:Photic Stimulation
keywords: Base rates; Bayesian analysis; Cognitive impairment; Malingering; Poor effort