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Use of multiple performance and symptom validity measures: Determining the optimal per test cutoff for determination of invalidity, analysis of skew, and inter-test correlations in valid and invalid performance groups

机译:使用多种性能和症状有效性措施:确定每个测试截止的最佳截止,用于确定无效性,偏差分析以及有效和无效性能组中的测试间相关性

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Objective: Discrimination of patients passing vs. failing the Word Memory Test (WMT) by performance on 11 performance and symptom validity tests (PVTs, SVTs) from the Meyers Neuropsychological Battery (MNB) at per-test false positive cutoffs ranging from 0 to 15%. PVT and SVT intercorrelation in subgroups passing and failing the WMT, as well as the degree of skew of the individual PVTs and SVT in the pass/fail subgroups, were also analyzed. Method: In 255 clinical and forensic cases, 100 failed and 155 passed the WMT, at a base-rate of invalid performance of 39.2%. Performance was contrasted on 10 PVTs and 1 SVT from the MNB, using per-test false positive rates of 0.0%, 3.3%, 5.0%, 10.0%, and 15.0% in discriminating WMT pass and WMT fail groups. These two WMT groups were also contrasted using the 10 PVTs and 1 SVT as continuous variables in a logistic regression. Results: The per-PVT false positive rate of 10% yielded the highest WMT pass/fail classification, and more closely approximated the classification obtained by logistic regression than other cut scores. PVT and SVT correlations were higher in cases failing the WMT, and data were more highly skewed in those passing the WMT. Conclusions: The optimal per-PVT and SVT cutoff is at a false positive rate of 10%, with failure of >= 3 PVTs/SVTs out of 11 yielding sensitivity of 61.0% and specificity of 90.3%. PVTs with the best classification had the greatest degree of skew in the WMT pass subgroup.
机译:目的:通过Peyers神经心理电池(MNB)的11个性能和症状有效性测试(PVTS,SVTS)在每次测试的假截面范围为0到15的情况下,通过性能辨别VS的患者的歧视失败的患者。 %。还分析了亚组中的PVT和SVT互相关,并且还分析了通过/失败亚组中单个PVT和SVT的偏差度。方法:在255例临床和法医案件中,100例失败,155次,以39.2%的无效性能的基本速率。性能与MNB的10个PVT和1SVT形成对比,使用每次测试假阳性率为0.0%,3.3%,5.0%,10.0%和15.0%,在识别WMT通过和WMT失败组。这两个WMT基团也使用10pvts和1 svt作为逻辑回归中的连续变量对比。结果:每PVT假阳性率为10%,产生了最高的WMT通过/失败分类,并且更接近逻辑回归获得的分类而不是其他削减分数。在发生WMT的情况下,PVT和SVT相关性较高,并且在通过WMT的那些中更高度偏斜。结论:最佳PER-PVT和SVT截止值为误率为10%,失效> = 3 PVTS / SVTS的11个产生敏感性为61.0%,特异性为90.3%。 PVTS具有最佳分类在WMT Pass子组中具有最大程度的偏斜。

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