Using Likelihood Ratios to Detect Invalid Performance with Performance Validity Measures

Abstract

Larrabee (2008) applied chained likelihood ratios to selected performance validity measures (PVMs) to identify non-valid performances on neuropsychological tests. He presented a method of combining different PVMs with different sensitivities and specificities into an overall probability of non-validity. We applied his methodology to a set of 11 PVMs using a sample of 255 subjects. The results of the study show that in various combinations of two or three PVMs, a high reliability of invalidity can be determined using the chained likelihood ratio method. This study advances the ability of clinicians to chain various PVMs together and calculate the probability that a set of data is invalid.

Associations between neuropsychiatric and health status outcomes in individuals with probable mTBI

“…Embedded performance validity metrics were used to evaluate effort and validity during testing, and included the Trail Making Test (total time > 170), BVMT-R (recognition hits < 5), and RAVLT-2 recognition (<10) (Denning, 2012;Shura et al, 2016;Whitney and Davis, 2015). Individuals were required to pass at least two of the three embedded validity measures to be included in the neuropsychological testing analysis ( (Larrabee, 2008;Meyers et al, 2014;Meyers and Volbrecht, 2003) in cases where data from one of the validity tests was missing, participants were required to pass all validity tests given; a total of 30 participants failed only on PVT: HC n = 7, mTBI n = 16, mTBI/PTSD n = 7).…”

Towards Balanced VA and SSA Policies in Psychological Injury Disability Assessment

“…For example, Larrabee (2012) advocated for three test failures being sufficient in this regard (also see Meyers, Miller, Thompson, Scalese, Allred, Rupp, Dupaix, & Junghyun Lee, 2014), and others query whether even two are sufficient (see Young, 2014a). However, the type of statistical calculation involved in supporting these conclusions, based on chained likelihood ratios and positive predictive power, might potentiate type I error, or falsely attributing malingering when it is not justified simply because many tests are given and just by chance two or more might emerge significant in any one assessment.…”

Normative comparisons for large neuropsychological test batteries: User-friendly and sensitive solutions to minimize familywise false positives

“…The familywise error issue also is prominent in the normative comparisons context (e.g., Berthelson, Mulchan, Odland, Miller, & Mittenberg, 2013;Bilder, Sugar, & Hellemann, 2014;Brooks, 2010;Crawford et al, 2007;Davis & Millis, 2014;Larrabee, 2008Larrabee, , 2014Loewenstein et al, 2006;Meyers et al, 2014;Naglieri & Paolitto, 2010;Palmer, Boone, Lesser, & Wohl, 1998;Proto et al, 2014;Schretlen et al, 2008). It has been argued that in clinical practice, lack of control over familywise error in normative comparisons may result in overdiagnosis and unnecessary treatment, increasing patient burden and unnecessary costs to the health care system (Binder et al, 2009;Brooks, Iverson, Holdnack, & Feldman, 2008;Gisslén, Price, & Nilsson, 2011;Torti, Focà, Cesana, & Lescure, 2011).…”