Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ijrobp.2009.12.024
Title: On the Importance of Accounting for Competing Risks in Pediatric Brain Cancer: II. Regression Modeling and Sample Size
Authors: Tai, B.-C. 
Grundy, R.
Machin, D.
Keywords: Cause-specific hazards
Competing risks
Subdistribution hazards
Issue Date: 2011
Source: Tai, B.-C., Grundy, R., Machin, D. (2011). On the Importance of Accounting for Competing Risks in Pediatric Brain Cancer: II. Regression Modeling and Sample Size. International Journal of Radiation Oncology, Biology, Physics 79 (4) : 1139-1146. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ijrobp.2009.12.024
Abstract: Purpose: To accurately model the cumulative need for radiotherapy in trials designed to delay or avoid irradiation among children with malignant brain tumor, it is crucial to account for competing events and evaluate how each contributes to the timing of irradiation. An appropriate choice of statistical model is also important for adequate determination of sample size. Methods and Materials: We describe the statistical modeling of competing events (A, radiotherapy after progression; B, no radiotherapy after progression; and C, elective radiotherapy) using proportional cause-specific and subdistribution hazard functions. The procedures of sample size estimation based on each method are outlined. These are illustrated by use of data comparing children with ependymoma and other malignant brain tumors. The results from these two approaches are compared. Results: The cause-specific hazard analysis showed a reduction in hazards among infants with ependymoma for all event types, including Event A (adjusted cause-specific hazard ratio, 0.76; 95% confidence interval, 0.45-1.28). Conversely, the subdistribution hazard analysis suggested an increase in hazard for Event A (adjusted subdistribution hazard ratio, 1.35; 95% confidence interval, 0.80-2.30), but the reduction in hazards for Events B and C remained. Analysis based on subdistribution hazard requires a larger sample size than the cause-specific hazard approach. Conclusions: Notable differences in effect estimates and anticipated sample size were observed between methods when the main event showed a beneficial effect whereas the competing events showed an adverse effect on the cumulative incidence. The subdistribution hazard is the most appropriate for modeling treatment when its effects on both the main and competing events are of interest. © 2010 Elsevier Inc. All rights reserved.
Source Title: International Journal of Radiation Oncology, Biology, Physics
URI: http://scholarbank.nus.edu.sg/handle/10635/24576
ISSN: 03603016
DOI: 10.1016/j.ijrobp.2009.12.024
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