Exploring individual change.
Exploring individual change.
复制标题
探索个体的改变。
DOI：
10.1037//0022-006x.66.5.838
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发表时间：
1998
影响因子：
5.9
通讯作者：
Lutz,W
中科院分区：
文献类型：
--
作者：
Krause,MS;Howard,KI;Lutz,W
在分析临床干预的影响时，公认的观点是，治疗后评分应用于评价治疗结局，治疗前评分通过随机分配或统计学偏分等同。然而，治疗后评分通常并不比变化评分更可靠，也不等同于变化评分，即使治疗前评分偏离两者。此外，还有一些数据分析方法可以显示个体患者的变化，即随时间变化的反应曲线，而不仅仅是显示群体的平均变化。这些方法将研究人员带回到他们应该用来选择要使用的特定变化模型的个人数据。为了最大限度地提高临床实践的相关性，治疗研究的结果应始终在最细分或个体变化水平上报告，并在适当时在更汇总的统计水平上报告。
In the analysis of the impact of clinical interventions, the received wisdom has been that posttreatment scores, with pretreatment scores equated by random assignment or statistically partialed out, should be used to evaluate treatment outcomes. However, posttreatment scores are not generally more reliable than, nor equivalent to, change scores, even with pretreatment scores partialed out of both. Moreover, there are data-analytic methods that indicate how individual patients change, in terms of response curves over time, rather than indicate only how much groups change on the average. These methods take researchers back to the individual data that they ought to use for choosing the specific models of change to be used. To maximize relevance for clinical practice, the results of treatment research should always be reported at this most disaggregated or individual change level, as well as, when appropriate, at more aggregated statistical levels.