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Dear Allstat members, During RCTs here, we routinely collect patient data at baseline, prior to treatment, then again at some specified endpoint. Often this data is ordinal (e.g. Quality of Life data, in which responses to questionnaire data are often scored 0=no, 1=yes, and then a total score is obtained). In order to compare the change in scores of patients receiving either drug therapy or placebo, with parametric data we would subtract the baseline value from the endpoint value for each patient, giving a 'change score', then compare these change scores between treatment groups with an independent t-test (or ANCOVA) as appropriate. However, when analysing ordinal data, it is my understanding that it is not valid to calculate change scores. Ordinal scales may measure a continuous underlying construct, but obviously the intervals between values on the scale are not necessarily consistent, so the 'true' distance between them might look like this: [1.........2.3.4.....5..6..7......8.9.10] On this basis, a patient whose score increases from 1 to 2 may have shown an absolute increase in the underlying variable of interest (e.g. quality of life) that is greater than the absolute increase made by a patient whose score goes up from 2 to 4. However, if one calculates a change score for each, then the opposite trend will be recorded: the first patient's change score will be lower than that of the second patient. Despite this, I regularly see change scores for ordinal variables in published papers. What confuses me is that the calculations underlying the Wilcoxon signed rank test essentially rely on change scores, and I had assumed this was a suitable test for ordinal data. If I want to see whether QoL has significantly improved within my placebo group during 'treatment', for example, the Wilcoxon test will subtract the first measurement from the second, then rank the differences, then add together the negative and positive ranks to obtain a test statistic. Is this process not going to be vulnerable to these same problems caused by the potential difference between the change in score and the absolute change in the underlying variable? My question really boils down to this: what is the best way to compare the change in an ordinal scale between patient groups? Is it valid to calculate change scores and perform Mann-Whitney U tests on those? If not, is it also not valid therefore to perform Wilcoxon tests to look for within group differences? I would greatly appreciate your advice, Thanks, Liz Hensor Dr Elizabeth M A Hensor PhD Data Analyst Academic Unit of Musculoskeletal and Rehabilitation Medicine 36 Clarendon Road Leeds West Yorkshire LS2 9NZ Tel: +44 (0) 113 3434944 Fax: +44 (0) 113 2430366 [log in to unmask]