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Determining the most appropriate components for a composite clinical trial outcome
M Angelyn Bethel1, Rury Holman, Steven M Haffner
1Division of Endocrinology, Department of Medicine, Duke University Medical Center, Durham, NC, USA.
American Heart Journal
|October 18, 2008
Summary
Composite endpoints in cardiovascular trials may not increase statistical power. Including events less affected by treatment can reduce precision and mask treatment effects, impacting study results.
Area of Science:
- Cardiovascular research
- Clinical trial methodology
- Statistical analysis in medicine
Background:
- Composite endpoints are often used in clinical trials to increase event rates and statistical power.
- However, the effectiveness of composite endpoints depends on the intervention's impact on each component.
- Treatment effect size can be influenced by the specific components included in a composite endpoint.
Purpose of the Study:
- To investigate the effect of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers on individual cardiovascular outcomes.
- To evaluate how different composite cardiovascular endpoints affect statistical power using data from the NAVIGATOR trial.
Main Methods:
- A meta-analysis was performed to determine treatment effects on individual cardiovascular outcomes.
- These effects were applied to two distinct composite cardiovascular outcomes.
- Analysis was conducted on a time-to-first-event basis using blinded data from the NAVIGATOR trial.
Main Results:
- Angiotensin-converting enzyme/angiotensin receptor blocker treatment showed significant odds reductions for CV death (13%), nonfatal myocardial infarction (16%), nonfatal stroke (14%), and heart failure (28%).
- Smaller but significant reductions were observed for hospitalization for angina (7%), while revascularization showed a non-significant reduction (5%).
- A narrower composite endpoint (CV death, MI, stroke, HF) showed a larger projected odds reduction (17.8%) compared to an extended composite (11.7%), indicating reduced power with the latter.
Conclusions:
- While composite endpoints increase event rates, they do not invariably enhance statistical power.
- Incorporating events minimally affected by an intervention can decrease the composite endpoint's precision.
- This masking effect can obscure true treatment effects, highlighting the importance of careful endpoint selection.
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