Normative data and validation of a regression based summary score for assessing meaningful neuropsychological change.

Normative data and validation of a regression based summary score for assessing meaningful neuropsychological change.
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用于评估有意义的神经心理学变化的基于回归的摘要得分的规范数据和验证。

DOI:
10.1080/13803395.2010.535504
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发表时间:
2011-06
影响因子:
2.2
通讯作者:
HNRC Group
HNRC Group
中科院分区:
心理学4区
文献类型:
--
作者:
Cysique LA;Franklin D Jr;Abramson I;Ellis RJ;Letendre S;Collier A;Clifford D;Gelman B;McArthur J;Morgello S;Simpson D;McCutchan JA;Grant I;Heaton RK;CHARTER Group;HNRC Group

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纵向认知变化的可靠检测和量化在许多神经系统疾病中具有相当重要的意义,特别是监测疾病进展和治疗对中枢神经系统的影响。在目前的研究中,我们使用改良的基于标准回归(SRB)的方法在一个样本中开发了重复神经心理学(NP)评估(6次测试)的规范性数据,该样本包括HIV未感染者(HIV-,N=172)和神经医学稳定的HIV感染者(HIV+,N=124)。先前的分析表明,感染和未感染的参与者之间的NP变化没有差异。变化的标准包括使用分层回归对发现显著影响随访表现的因素进行校正。随访表现的最稳健和一致的预测因素是相同测试的先前表现(在所有模型中均有贡献)和先前整体NP能力的测量(所有模型中97%的预测因素)。人口统计学变量在所有模型中占10%-46%,并且数量很少;而重测间隔仅占所有模型的6%。根据回归方程,计算每个时间间隔的每个测试测量的标准化变化评分(z评分);然后对这些z评分取平均值,以创建总电池变化评分。一个独立的样本的HIV-参与者谁完成了15个测试中的8个被用来验证一个简短的总结变化分数。通过向第一作者发送电子邮件请求,可以获得电子格式的规范性数据。在基线后临床稳定的纵向队列中,基于规范性数据对实践影响的校正改善了NP损伤分类的一致性。
Reliable detection and quantification of longitudinal cognitive change are of considerable importance in many neurological disorders, particularly to monitor central nervous system effects of disease progression and treatment. In the current study, we developed normative data for repeated neuropsychological (NP) assessments (6 testings) using a modified Standard Regression-Based (SRB) approach in a sample that includes both HIV-uninfected (HIV−, N=172) and neuromedically stable HIV-infected (HIV+, N=124) individuals. Prior analyzes indicated no differences in NP change between the infected and uninfected participants. The norms for change included correction for factors found to significantly affect follow-up performance, using hierarchical regression. The most robust and consistent predictors of follow-up performance were the prior performance on the same test (which contributed in all models) and a measure of prior overall NP competence (predictor in 97% of all models). Demographic variables were predictors in 10%-46% of all models and in small amounts; while test retest interval contributed in only 6% of all models. Based on the regression equations, standardized change scores (z-scores) were computed for each test measure at each interval; these z scores were then averaged to create a total battery change score. An independent sample of HIV− participants who had completed 8 of the 15 tests was used to validate an abridged summary change score. The normative data are available in an electronic format by email request to the first author. Correction for practice effects based on normative data improved the consistency of NP impairment classification in a clinically stable longitudinal cohort after baseline.
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