Caronni Antonio, Sciumè Luciana
a Department of Neurorehabilitation Sciences , Casa di Cura del Policlinico , Milano , Italy.
Disabil Rehabil. 2017 Jun;39(13):1341-1347. doi: 10.1080/09638288.2016.1194486. Epub 2016 Jun 26.
The aim of the current work is to provide a novel method for demonstrating the modification of a single patient's performance on questionnaires and scales. The minimal detectable change (MDC), a statistics indicating the minimal change in measure not attributable to random variation, is commonly used in rehabilitation for this purpose. However, the MDC has some important drawbacks (e.g. it cannot be calculated on scores from ordinal tests and it can be only used for full questionnaire).
Review of the MDC and its limitations and application of the McNemar test on simulated data from single subjects.
We propose to use the McNemar test to check if the proportion of test items affirmed by a patient after rehabilitation is significantly different from the same proportion before rehabilitation. A significant McNemar test would indicate a non-random modification of the patient's score and thus a true modification of his/her performance.
The application of the McNemar test to questionnaires and scales offers a simple method for demonstrating the modification of a single patient's performance. This use of the McNemar test overcomes the weaknesses of the MDC and gives support to the clinician in assisting him/her to convincingly communicate a non-negligible modification of the patient's status. IMPLICATIONS FOR REHABILITATION Measuring the change in patients' status is of paramount importance in medicine and rehabilitation. However, tracking the change in rehabilitation is difficult. For example, the minimal detectable change cannot be calculated on scores from ordinal questionnaires and tests, which are widely used as rehabilitative outcome measures. We propose here to use a McNemar test to check if the proportion of test items affirmed or passed by is significantly different between two conditions (e.g. before vs. after rehabilitation). Similar to the minimal detectable change, the significant McNemar test would indicate a non-random modification of the patient's test score. In addition, the McNemar test can be calculated on ordinal data, thus overcoming some of the minimal detectable change weaknesses.
当前研究的目的是提供一种新方法，用于证明单个患者在问卷和量表上表现的改变。最小可检测变化（MDC）是一种统计量，用于表明测量中并非由随机变异导致的最小变化，常用于康复领域的这一目的。然而，MDC存在一些重要缺陷（例如，它不能根据有序测试的分数计算，且只能用于完整问卷）。
回顾MDC及其局限性，并将McNemar检验应用于单个受试者的模拟数据。
我们建议使用McNemar检验来检查患者康复后肯定的测试项目比例与康复前的相同比例是否有显著差异。显著的McNemar检验将表明患者分数的非随机改变，从而表明其表现的真正改变。
将McNemar检验应用于问卷和量表提供了一种简单方法，用于证明单个患者表现的改变。这种对McNemar检验的使用克服了MDC的弱点，并在协助临床医生令人信服地传达患者状态的不可忽略的改变方面提供了支持。对康复的启示在医学和康复中，测量患者状态的变化至关重要。然而，追踪康复中的变化很困难。例如，最小可检测变化不能根据广泛用作康复结果测量的有序问卷和测试的分数来计算。我们在此建议使用McNemar检验来检查在两种情况（例如康复前与康复后）下肯定或通过的测试项目比例是否有显著差异。与最小可检测变化类似，显著的McNemar检验将表明患者测试分数的非随机改变。此外，McNemar检验可以根据有序数据计算，从而克服了最小可检测变化的一些弱点。
