Hinton-Bayre Anton D
Arch Clin Neuropsychol. 2016 Nov 22;31(7):754-768. doi: 10.1093/arclin/acw064.
Several reliable change indices (RCIs) exist to evaluate statistically significant individual change with repeated neuropsychological assessment. Yet there is little guidance on model selection and subsequent implications. Using existing test-retest norms, key parameters were systematically evaluated for influence on different RCI models.
Normative test-retest data for selected Wechsler Memory Scale-IV subtests were chosen based on the direction and magnitude of differential practice (inequality of test and retest variance). The influence of individual relative position compared to the normative mean was systematically manipulated to evaluate for predictable differences in responsiveness for three RCI models.
With respect to negative change, RCI McSweeny was most responsive when individual baseline scores were below the normative mean, irrespective of differential practice. When an individual score was greater than the normative mean, RCI Chelune was most responsive with lower retest variance, and RCI Maassen most responsive with greater retest variance. This pattern of results can change when test-retest reliability is excellent and there is greater retest variability. Order of responsiveness is reversed if positive change is of interest.
RCI models tend to agree when the individual approximates the normative mean at baseline and test-retest variability is equal. However, no RCI model will be universally more or less responsive across all conditions, and model selection may influence subsequent interpretation of change. Given the systematic and predictable differences between models, a more rationale choice can now be made. While a consensus on RCI model preference does not exist, we prefer the regression-based model for several reasons outlined.
有几种可靠变化指数(RCI)可用于通过重复神经心理学评估来评估具有统计学意义的个体变化。然而,在模型选择及后续影响方面几乎没有指导意见。利用现有的重测常模,系统评估关键参数对不同RCI模型的影响。
根据差异练习的方向和程度(测试与重测方差的不平等),选择韦氏记忆量表第四版(Wechsler Memory Scale-IV)部分子测验的规范性重测数据。系统地操纵个体相对于常模均值的相对位置,以评估三种RCI模型在反应性方面的可预测差异。
关于负向变化,当个体基线分数低于常模均值时,无论差异练习如何,RCI麦克休尼(McSweeny)最具反应性。当个体分数高于常模均值时,重测方差较低时RCI切伦(Chelune)最具反应性,重测方差较大时RCI马森(Maassen)最具反应性。当重测信度极佳且重测变异性更大时,这种结果模式可能会改变。如果关注正向变化,反应性顺序会颠倒。
当个体在基线时接近常模均值且重测变异性相等时,RCI模型往往会达成一致。然而,没有一种RCI模型在所有情况下普遍更具或更不具反应性,模型选择可能会影响后续对变化的解释。鉴于模型之间存在系统且可预测的差异,现在可以做出更合理的选择。虽然对于RCI模型偏好尚未达成共识,但基于文中概述的几个原因,我们更倾向于基于回归的模型。