Table 1.

Multivariable regression models for competing risks endpoints and for the composite endpoint of the implantable cardioverter-defibrillator data

	Regression on the cause-specific hazard functiona [HR (95% CI); P-value]	Regression on the CIFb [sHR (95% CI); P-value]
Event: first appropriate ICD therapy
Covariates
Age (for each 10-year increase)	1.23 (1.07–1.41); P = 0.003	1.19 (1.05–1.36); P = 0.006
Secondary prevention (compared with primary)	2.29 (1.60–3.27); P < 0.001	2.23 (1.58–3.14); P < 0.0001
Event: death without prior ICD therapy
Covariates
Age (for each 10-year increase)	1.63 (1.11–2.39); P = 0.01	1.40 (0.92–2.13); P = 0.12
Secondary prevention (compared with primary)	1.25 (0.54–2.89); P = 0.60	0.92 (0.39–2.15); P = 0.85
Composite endpoint: first appropriate ICD therapy or prior death
Covariates
Age (for each 10-year increase)	1.28 (1.12–1.45); P < 0.001	1.28 (1.12–1.45); P < 0.001
Secondary prevention (compared with primary)	2.11 (1.52–2.93); P < 0.001	2.11 (1.52–2.93); P < 0.001

Note that the effect on the hazard function and the CIF is identical for the composite endpoint for which no competing risks are present.

HR, (cause-specific) hazard ratio; sHR, ratio of the subdistribution hazards; CI, confidence interval.

^aCox proportional hazards models for cause-specific hazards for competing risks endpoints. Cox proportional hazards model for the composite endpoint.

^bFine-Gray regression for competing risks endpoints. Cox proportional hazards model for the composite endpoint.