Background: Individual differences in treatment responses to health-related quality-of-life interventions arise ubiquitously and prominently in clinical trials. These differences do not reflect error, but instead represent patterns of response that vary reliably across patients. Individual differences complement and qualify the information conveyed by average health-related quality-of-life effects for treatment arms. If clinicians and patients are to use health-related quality-of-life findings from clinical trials to make treatment decisions, they must have information about the extent and nature of such individual variation. The relatively small effect sizes of health-related quality-of-life outcomes in clinical trials offer a weak basis for generalizing results to new persons, and patients routinely anticipate likely treatment benefits for themselves that exceed these small effects. A focus on individual differences in treatment response can promote more realistic appreciation of expected benefit and uncertainty. A pharmacogenetic example shows how individual differences in drug metabolism can directly affect health-related quality-of-life treatment outcomes such as pain and physical functioning.
Measures: The authors suggest how graphical displays can summarize individual responses and provide a context for interpreting the size and generality of the average treatment. Mixed-effects modeling subsumes average treatment differences and individual differences in a unified statistical analysis. Analysis of health-related quality-of-life data from an advanced colorectal cancer trial illustrates this approach. Objective statistical criteria indicate that, for this example, individual differences dominate the treatment difference.
Conclusions: The authors suggest that presentation of the spectrum of individual responses and associated prediction intervals can convey clinically meaningful information regarding the impact of a treatment on health-related quality of life.