A parametric analysis of ordinal quality-of-life data can lead to erroneous results

Abstract

Objective: Measurements from health-related quality-of-life (HRQoL) studies, although usually of an ordered categorical nature, are typically treated as continuous variables, allowing the calculation of mean values and the administration of parametric statistics, such as t-tests. We investigated whether parametric, compared to nonparametric, analyses of ordered categorical data may lead to different conclusions. Study Design and Setting: HRQoL data were obtained from patients with a diagnosis of asthma (n = 192) and chronic obstructive pulmonary disease (COPD; n = 88) at two time points. The impact of the group factor (asthma vs. COPD) and the time factor (t1 vs. t2) on HRQoL was analyzed with a metric approach (repeated measures ANOVA) and two ordinal approaches (each with a nonparametric repeated measures ANOVA). Results: Using the metric approach, a significant effect of "group" (P = 0.0061) and "time" (P = 0.0049) on HRQoL was found. The first ordinal approach (ranked total score) still showed a significant effect for "group" (P = 0.0033) with a worse HRQoL for patients suffering from COPD. In the second approach (ranks for each HRQoL item and summed ranks), there were no significant effects. Conclusion: Applying simple parametric methods to ordered categorical HRQoL scores led to different results from those obtained with nonparametric methods. In these cases, an ordinal approach will prevent inappropriate conclusions. (c) 2008 Elsevier Inc. All rights reserved.