Which health-related quality-of-life outcome when planning randomized trials: disease-specific or generic, or both? A common factor model.

The primary outcomes in trials are usually disease-specific measures (DSMs) designed to be responsive to changes in the condition caused by treatment. For purposes of cost-effectiveness analysis, treatment effects on the DSM are often "mapped" into treatment effects on a generic health-related quality-of-life (QOL) scale, such as EuroQol five-dimensional questionnaire. Trialists have the option of including generic QOL measures as trial outcomes. We consider the relative efficiency (estimate divided by its standard error) of treatment effects derived from the DSM, the generic QOL, the generic QOL indirectly estimated from the mapped DSM, and a pooled estimate combining the direct and indirect information on the generic QOL. By using a "common factor" theory of the relationship between the DSM and the generic QOL, we define the circumstances under which indirectly estimated generic QOL is more efficient than the direct one and when a pooled QOL estimate is more efficient than the DSM estimate. As long as the DSM is more responsive, there is always a threshold sample size above which the indirect estimate has better precision than the direct estimate. This threshold, however, increases as the (1) relative responsiveness ratio of the DSM to the generic QOL increases, (2) precision of the estimated mapping coefficient increases, and (3) true effect becomes smaller. The pooled estimate on the generic QOL may be more efficient than the DSM itself unless the reliability of the DSM is particularly high. Trials powered on DSMs are likely to have sufficient power to detect treatment effect on the generic QOL if a pooled estimate is used. We conclude that generic QOL instruments should be routinely included in randomized controlled trials. Information on mapping coefficients and on relative responsiveness should be collected more systematically to facilitate both evidence synthesis and trial design.