Comparing center-specific cumulative incidence functions

Ludi Fan () and Douglas E. Schaubel ()
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Ludi Fan: Eli Lilly and Company
Douglas E. Schaubel: University of Michigan

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2016, vol. 22, issue 1, No 2, 17-37

Abstract: Abstract The competing risks data structure arises frequently in clinical and epidemiologic studies. In such settings, the cumulative incidence function is often used to describe the ultimate occurrence of a particular cause of interest. If the objective of the analysis is to compare subgroups of patients with respect to cumulative incidence, imbalance with respect to group-specific covariate distributions must generally be factored out, particularly in observational studies. This report proposes a measure to contrast center- (or, more generally group-) specific cumulative incidence functions (CIF). One such application involves evaluating organ procurement organizations with respect to the cumulative incidence of kidney transplantation. In this case, the competing risks include (i) death on the wait-list and (ii) removal from the wait-list. The proposed method assumes proportional cause-specific hazards, which are estimated through Cox models stratified by center. The proposed center effect measure compares the average CIF for a given center to the average CIF that would have resulted if that particular center had covariate pattern-specific cumulative incidence equal to that of the national average. We apply the proposed methods to data obtained from a national organ transplant registry.

Keywords: Cox regression; Center effect; Competing risks; Cumulative incidence function; Kidney transplantation (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10985-015-9324-1

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