Department of Surgery, University of Queensland Medical School, Princess Alexandra Hospital, Brisbane, Australia.
Arch Clin Neuropsychol. 2010 May;25(3):244-56. doi: 10.1093/arclin/acq008. Epub 2010 Mar 2.
The use of reliable change (RC) statistics to determine whether an individual has significantly improved or deteriorated on retesting is growing rapidly in clinical neuropsychology. This paper demonstrates how with only basic test-retest data and a series of simple expressions, the clinician/researcher can implement the majority of contemporary RC model(s). Though sharing a fundamental structure, RC models vary in how they derive predicted retest scores and standard error terms. Published test-retest normative data and a simple case study are presented to demonstrate how to calculate several well-known RC scores. The paper highlights the circumstances under which models will diverge in the estimation of RC. Most importantly variations in individual's performance relative to controls at initial testing, practice effects, inequality of control variability from test to retest, and degree of reliability will see systematic and predictable disagreement among models. More generally, the limitations and opportunities of RC methodology were discussed. Although a consensus on preferred model continues to be debated, the comparison of RC models in clinical samples is encouraged.
在临床神经心理学中，使用可靠变化（RC）统计来确定个体在重测时是否有显著改善或恶化正在迅速发展。本文展示了临床医生/研究人员如何仅使用基本的测试-重测数据和一系列简单的表达式，即可实现大多数当代 RC 模型。虽然 RC 模型具有基本相同的结构，但它们在如何得出预测重测分数和标准误差项方面存在差异。本文提供了发表的测试-重测常模数据和一个简单的案例研究，以演示如何计算几种著名的 RC 分数。本文强调了在哪些情况下模型会在 RC 的估计中出现分歧。最重要的是，个体在初始测试时相对于对照组的表现、练习效应、从测试到重测的控制变异性的不平等以及可靠性的程度，将导致模型之间出现系统和可预测的分歧。更一般地，本文讨论了 RC 方法的局限性和机遇。尽管关于首选模型的共识仍在争论中，但鼓励在临床样本中比较 RC 模型。
