Composite outcomes: weighting component events according to severity assisted interpretation but reduced statistical power

Abstract

Objective: In trials of chronic disease therapy, each patient may experience several nonfatal illnesses and death. "Composite" outcome measures combine information from these different components of disease burden. Most common is the binary distinction between patients undergoing one or more events and those undergoing no events. We compare this approach with a composite score that preserves information on the number and severity of events.

Study design and setting: The binary composite measure and composite score were derived for each patient in a trial of cardiovascular therapy. All nonfatal events contributed to the composite score according to their severity: recurrent myocardial infarction (weight 0.5), congestive heart failure that required the use of open-label angiotensin-converting enzyme (ACE) inhibitors (weight 0.2), and hospitalization to treat congestive heart failure (weight 0.5).

Results: In the example data set, the composite score required a 10% larger sample size to achieve the same power as the binary measure. However, the composite score suggested that the treatment impacted on the first nonfatal event and mortality only.

Conclusions: The composite score provides a more informative measure of disease burden and may avoid overestimating the evidence supporting a treatment effect when that evidence is largely from less severe early events.