Facility profiling under competing risks using multivariate prognostic scores: Application to kidneytransplant centers

2021 ◽  
pp. 096228022110528
Author(s):  
Youjin Lee ◽  
Douglas E Schaubel

The performance of health care facilities (e.g. hospitals, transplant centers, etc.) is often evaluated through time-to-event outcomes. In this paper, we consider the case where, for each subject, the failure event is due to one of several mutually exclusive causes (competing risks). Since the distribution of patient characteristics may differ greatly by the center, some form of covariate adjustment is generally necessary in order for center-specific outcomes to be accurately compared (to each other or to an overall average). We propose a weighting method for comparing facility-specific cumulative incidence functions to an overall average. The method directly standardizes each facility’s non-parametric cumulative incidence function through a weight function constructed from a multivariate prognostic score. We formally define the center effects and derive large-sample properties of the proposed estimator. We evaluate the finite sample performance of the estimator through simulation. The proposed method is applied to the end-stage renal disease setting to evaluate the center-specific pre-transplant mortality and transplant cumulative incidence functions from the Scientific Registry of Transplant Recipients.

2013 ◽  
Vol 66 (6) ◽  
pp. 648-653 ◽  
Author(s):  
Aurelien Latouche ◽  
Arthur Allignol ◽  
Jan Beyersmann ◽  
Myriam Labopin ◽  
Jason P. Fine

Author(s):  
Paul C. Lambert

Competing risks occur in survival analysis when an individual is at risk of more than one type of event and one event's occurrence precludes another's. The cause-specific cumulative incidence function (CIF) is a measure of interest with competing-risks data. It gives the absolute (or crude) risk of having the event by time t, accounting for the fact that it is impossible to have the event if a competing event occurs first. The user-written command stcompet calculates nonparametric estimates of the cause-specific CIF, and the official Stata command stcrreg fits the Fine and Gray (1999, Journal of the American Statistical Association 94: 496–509) model for competing-risks data. Geskus (2011, Biometrics 67: 39–49) has recently shown that standard software can estimate some of the key measures in competing risks by restructuring the data and incorporating weights. This has a number of advantages because any tools developed for standard survival analysis can then be used to analyze competing-risks data. In this article, I describe the stcrprep command, which restructures the data and calculates the appropriate weights. After one uses stcrprep, a number of standard Stata survival analysis commands can then be used to analyze competing risks. For example, sts graph, failure will give a plot of the cause-specific CIF, and stcox will fit the Fine and Gray (1999) proportional subhazards model. Using stcrprep together with stcox is computationally much more efficient than using stcrreg. In addition, stcrprep opens up new opportunities for competing-risk models. I illustrate this by fitting flexible parametric survival models to the expanded data to directly model the cause-specific CIF.


2015 ◽  
Vol 7 (3) ◽  
pp. 282-293 ◽  
Author(s):  
Federico Bonofiglio ◽  
Jan Beyersmann ◽  
Martin Schumacher ◽  
Michael Koller ◽  
Guido Schwarzer

2008 ◽  
Vol 26 (24) ◽  
pp. 4027-4034 ◽  
Author(s):  
James J. Dignam ◽  
Maria N. Kocherginsky

In clinical cancer research, competing risks are frequently encountered. For example, individuals undergoing treatment for surgically resectable disease may experience recurrence near the removed tumor, metastatic recurrence at other sites, occurrence of second primary cancer, or death resulting from noncancer causes before any of these events. Two quantities, the cause-specific hazard function and the cumulative incidence function, are commonly used to summarize outcomes by event type. Tests for event-specific differences between treatment groups may thus be based on comparison of (a) cause-specific hazards via a log-rank or related test, or (b) the cumulative incidence functions via one of several available tests. Inferential results for tests based on these different metrics can differ considerably for the same cause-specific end point. Depending on the questions of principal interest, one or both metrics may be appropriate to consider. We present simulation study results and discuss examples from cancer clinical trials to illustrate these points and provide guidance for analysis when competing risks are present.


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