Article

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). METHOD:
Review of the MDC and
its limitations and application of the McNemar test on simulated data from single
subjects. RESULTS: 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. CONCLUSIONS: 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.

Langue : ANGLAIS