Analyzing composite outcomes in cardiovascular studies: traditional Cox proportional hazards versus quality-of-life-adjusted survival approaches.

Abstract

Background: Composite outcomes that weight each component equally are commonly used to study treatment effects. We hypothesized that each component of a composite outcome would differentially affect patients' overall health-related quality of life (HRQL).

Methods: We tested our hypothesis using data from 2 published clinical studies of treatment for heart failure, one comparing metformin and sulfonylurea and the other comparing digoxin and placebo. We applied the quality-adjusted survival (QAS) approach, which incorporates HRQL data to accommodate differential weights for 2 components (in this analysis, death or admission to hospital) of a commonly used composite end point. For each of the 2 studies, the composite outcome was partitioned into its components, to which utility weights derived from the literature were assigned. Total QAS time determined for each treatment by the QAS analysis was compared with the results from traditional survival analyses based on Cox proportional hazards regression.

Results: In the observational study of metformin in heart failure, the risk of the composite outcome of death or admission to hospital was lower for those receiving metformin therapy than for those who received sulfonylurea (event rate 160 [77%] v. 658 [85%]; hazard ratio [HR] 0.83, 95% confidence interval [CI] 0.70-0.99). With traditional survival analysis, the net gain was 0.82 years (95% CI 0.26-1.37), whereas the difference in QAS time was less, at 0.54 years (95% CI 0.20-0.89). In the randomized trial of digoxin therapy, the risk of the composite outcome was lower for those receiving the intervention than for those receiving placebo (event rate 1291 [38%] v. 1041 [31%]; HR 0.75, 95% CI 0.69-0.82). With traditional survival analysis, the net gain was 0.06 years (95% CI 0.02-0.16), whereas the difference in QAS time was greater, at 0.11 years (95% CI 0.06-0.16).

Interpretation: Studies that assume equal weights for the components of composite outcomes may overestimate or underestimate treatment effects. By incorporating HRQL into survival analyses, the impact of the various components of the outcome can be assessed more directly.