Methods to explain the clinical significance of health status measures
Methods to explain the clinical significance of health status measures
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DOI：
10.4065/77.4.371
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发表时间：
2002-04-01
影响因子：
8.9
通讯作者：
Norman, GR
中科院分区：
文献类型：
--
作者：
Guyatt, GH;Osoba, D;Norman, GR
人们可以将建立生活质量测量的可解释性的方法分类为基于锚的或基于分布的。以锚为基础的措施需要一个独立的标准或锚，它本身是可解释的，至少与正在探索的文书有适度的相关性。人们可以进一步将以锚为基础的办法分为以人口为重点的措施和以个人为重点的措施。以人群为中心的方法类似于构建验证，并依赖于多个锚，这些锚根据整个人群来框定个人的反应(例如，一组得分为40的患者的死亡率为20%)。基于人群的方法包括单个项目的状态、诊断、症状、疾病严重性和对治疗的反应，以个体为重点的方法类似于标准验证。这些方法依赖于一个锚，并且建立了分数变化中最小的重要差异，需要两个步骤。第一步是确定患者平均认为重要的分数的最小变化(最小重要差异)。第二步是估计达到最小重要差异的患者的比例。这种以个体为中心的方法包括对患者内部变化的全球评级和对患者之间差异的全球评级。基于分布的方法依赖于根据结果的基本分布来表达效果。研究人员可以用人与人之间的标准偏差单位、人内的标准偏差单位和测量的标准误差来表示影响。没有一种单一的可解释性方法是完美的。使用多种策略可能会提高任何特定文书的可解释性。
One can classify ways to establish the interpretability of quality-of-life measures as anchor based or distribution based. Anchor-based measures require an independent standard or anchor that is itself interpretable and at least moderately correlated with the instrument being explored. One can further classify anchor-based approaches into population-focused and individual-focused measures. Population-focused approaches are analogous to construct validation and rely on multiple anchors that frame an individual's response in terms of the entire population (eg, a group of patients with a score of 40 has a mortality of 20%). Anchors for population-based approaches include status on a single item, diagnosis, symptoms, disease severity, and response to treatment, Individual-focused approaches are analogous to criterion validation. These methods, which rely on a single anchor and establish a minimum important difference in change in score, require 2 steps. The first step establishes the smallest change in score that patients consider, on average, to be important (the minimum important difference). The second step estimates the proportion of patients who have achieved that minimum important difference. Anchors for the individual-focused approach include global ratings of change within patients and global ratings of differences between patients. Distribution-based methods rely on expressing an effect in terms of the underlying distribution of results. Investigators may express effects in terms of between-person standard deviation units, within-person standard deviation units, and the standard error of measurement. No single approach to interpretability is perfect. Use of multiple strategies is likely to enhance the interpretability of any particular instrument.