Problems in measuring or interpreting change in patient outcomes

Determining the effectiveness of interventions in osteoarthritis (OA) is often based on the measurement of change in outcomes such as pain, disability and quality of life. In calculating and interpreting change in outcomes, the assumption is that any change (greater than the measurement error for the instrument) represents a real change in outcome (i.e. a real reduction in pain or disability or a real increase in quality of life). In a clinical trial such change is largely attributed to the efficacy of the intervention. The fundamental principle underlying this assessment of change is that an individual's attitude towards a particular construct and their way of calibrating it will remain stable. For example, that a visual analogue pain score (VAS) of 63 mm obtained before treatment will mean the same as a VAS pain score of 63 mm obtained after treatment. However, there is increasing evidence that many patient-perceived outcomes are not stable over time but alter in response to adaptation to illness or symptoms, coping strategies, and changes in expectations resulting from experience (of treatment, other illness and social or demographic factors such as age). Indeed, the ability to recalibrate symptoms such as pain, or to change expectations and definitions about what constitutes quality of life are often viewed as desirable attributes in chronic disease, representing successful coping or adaptation to changed circumstances. Why do we recognize, and welcome, dynamism in patient-centered outcomes in clinical practice but ignore it when making assessments of treatment efficacy? One explanation for this apparent discrepancy is that clinical impressions of the patient's well-being, standardized measures of outcome based on sound psychometric principles and statistical methods used to calculate change in outcome have largely developed as discrete methods of assessment with their own underlying principles, rules and assumptions. Few observers have recognized the importance of their clinical observations about adaptation to the measurement of outcome or the interpretation of statistical analyses of change. There is, however, a growing interest in highlighting and explaining these relationships and in developing statistical methods for measuring change(1-3) that have important applications to the assessment of treatment efficacy in arthritis.