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Estimating risk of venous thromboembolism in patients with cancer in the presence of competing mortality
Author(s) -
Ay C.,
Posch F.,
Kaider A.,
Zielinski C.,
Pabinger I.
Publication year - 2015
Publication title -
journal of thrombosis and haemostasis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.947
H-Index - 178
eISSN - 1538-7836
pISSN - 1538-7933
DOI - 10.1111/jth.12825
Subject(s) - medicine , cumulative incidence , hazard ratio , proportional hazards model , incidence (geometry) , log rank test , venous thromboembolism , cancer , cumulative risk , gastroenterology , surgery , thrombosis , confidence interval , cohort , physics , optics
Summary Background In studies on cancer‐associated venous thromboembolism ( VTE ), patients not only are at risk for VTE but also may die from their underlying malignancy. Objectives In this competing‐risk ( CR ) scenario, we systematically compared the performance of standard (Kaplan–Meier estimator [1‐ KM ]), log‐rank test, and Cox model) and specific CR methods for time‐to‐ VTE analysis. Patients and Methods Cancer patients (1542) were prospectively followed for a median of 24 months. VTE occurred in 112 (7.3%) patients, and 572 (37.1%) patients died. Results In comparison with the CR method, 1‐ KM slightly overestimated the cumulative incidence of VTE (cumulative VTE incidence at 12 and 24 months [1‐ KM vs. CR ]: 7.22% vs. 6.74%, and 8.40% vs. 7.54%, respectively). Greater bias was revealed in tumor entities with high early mortality (e.g., pancreatic cancer, n = 99, 24‐month cumulative VTE incidence: 28.37% vs. 19.30%). Comparing the (subdistribution) hazard of VTE between patients with low and high baseline D‐dimer, the Cox model yielded a higher estimate than the corresponding CR model (hazard vs. subdistribution hazard ratio [95% CI ] 2.85 [1.92–4.21] vs. 2.47 [1.67–3.65]). For this comparison, the log‐rank test yielded a higher test statistic and smaller P ‐value than Gray's test (χ 2 on 1 degree of freedom: 29.88 vs. 21.34). Conclusion In patients with cancer who are at risk for VTE and death, standard and CR methods for time‐to‐ VTE analysis can generate differing results. For 1‐ KM , the magnitude of bias is a direct function of competing mortality. Consequently, bias tends to be negligible in cancer patient populations with low mortality but can be considerable in populations at high risk of death.