Methods to evaluate risks for composite end points and their individual components.
OBJECTIVE
Both randomized and observational studies commonly examine composite end points, but the literature on model development and criticism in this setting is limited.


METHODS
We examined approaches for evaluating heterogeneity in the effects of risk factors for different components of the end point, and determining the impact of heterogeneity on the ability to predict the composite end point. A specific example considered the composite cardiovascular disease end point in the Physicians' Health Study that occurred in 1,542 (myocardial infarction, n = 716; stroke, n = 557; cardiovascular death, n = 269) of 16,688 participants with complete information on baseline covariates. The strategy compared alternative polytomous logistic regression models assuming different effects of risk factors on components of the end point and a comparable logistic model assuming common effects.


RESULTS
Likelihood ratio tests identified heterogeneity in the effects of age, alcohol consumption, and diabetes across components of the outcome, but comparability in the effects of other risk factors. However, a model assuming uniform effects explained over 90% of the log-likelihood change in the best polytomous model, and the two models also performed similarly based on a comparison of ROC curves.


CONCLUSIONS
The overall strategy may be helpful for evaluating the validity of a composite end point analysis and identifying heterogeneity in risk factors.
Glynn
Robert J
RJ
Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health, 900 Commonwealth Avenue East, Boston, MA 02215-1204, USA. rglynn@rics.bwh.harvard.edu
Rosner
Bernard
B
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000627:therapeutic use; D000975:Antioxidants / Q000627:therapeutic use; D001241:Aspirin / Q000627:therapeutic use; D002318:Cardiovascular Diseases / Q000517:prevention & control; D006306:Health Surveys; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000517:prevention & control; D018570:Risk Assessment; D019207:beta Carotene / Q000627:therapeutic use
D000894:Anti-Inflammatory Agents, Non-Steroidal; D000975:Antioxidants; D019207:beta Carotene; D001241:Aspirin