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. [Copyright &y& Elsevier]