Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical Perspectives
Modern competing risks analysis has 2 primary goals in clinical epidemiology as follows: (i) to maximize the clinician’s knowledge of etiologic associations existing between potential predictor variables and various cause-specific outcomes via cause-specific hazard models, and (ii) to maximize the c...
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Elsevier
2024-06-01
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| Series: | Kidney International Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468024924000627 |
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| author | Jeffrey J. Gaynor Giselle Guerra Rodrigo Vianna Marina M. Tabbara Enric Lledo Graell Gaetano Ciancio |
| author_facet | Jeffrey J. Gaynor Giselle Guerra Rodrigo Vianna Marina M. Tabbara Enric Lledo Graell Gaetano Ciancio |
| author_sort | Jeffrey J. Gaynor |
| collection | DOAJ |
| description | Modern competing risks analysis has 2 primary goals in clinical epidemiology as follows: (i) to maximize the clinician’s knowledge of etiologic associations existing between potential predictor variables and various cause-specific outcomes via cause-specific hazard models, and (ii) to maximize the clinician’s knowledge of noteworthy differences existing in cause-specific patient risk via cause-specific subdistribution hazard models (cumulative incidence functions [CIFs]). A perfect application exists in analyzing the following 4 distinct outcomes after listing for a deceased donor kidney transplant (DDKT): (i) receiving a DDKT, (ii) receiving a living donor kidney transplant (LDKT), (iii) waitlist removal due to patient mortality or a deteriorating medical condition, and (iv) waitlist removal due to other reasons. It is important to realize that obtaining a complete understanding of subdistribution hazard ratios (HRs) is simply not possible without first having knowledge of the multivariable relationships existing between the potential predictor variables and the cause-specific hazards (perspective #1), because the cause-specific hazards form the “building blocks” of CIFs. In addition, though we believe that a worthy and practical alternative to estimating the median waiting-time-to DDKT is to ask, “what is the conditional probability of the patient receiving a DDKT, given that he or she would not previously experience one of the competing events (known as the cause-specific conditional failure probability),” only an appropriate estimator of this conditional type of cumulative incidence should be used (perspective #2). One suggested estimator, the well-known “one minus Kaplan-Meier” approach (censoring competing events), simply does not represent any probability in the presence of competing risks and will almost always produce biased estimates (thus, it should never be used). |
| format | Article |
| id | doaj-art-86725e70cadd424daced61bb30c770fb |
| institution | Kabale University |
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| language | English |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kidney International Reports |
| spelling | doaj-art-86725e70cadd424daced61bb30c770fb2025-08-20T03:45:07ZengElsevierKidney International Reports2468-02492024-06-01961580158910.1016/j.ekir.2024.01.050Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical PerspectivesJeffrey J. Gaynor0Giselle Guerra1Rodrigo Vianna2Marina M. Tabbara3Enric Lledo Graell4Gaetano Ciancio5Department of Surgery, Miami Transplant Institute, University of Miami Miller School of Medicine; Miami, Florida, USA; Correspondence: Jeffrey J. Gaynor, Miami Transplant Institute, Dewitt Daughtry Family Department of Surgery, University of Miami Miller School of Medicine, Highland Professional Building, 1801 NW 9th Avenue, Rm. 4028, Miami, Florida 33136, USA.Department of Medicine, Miami Transplant Institute, University of Miami Miller School of Medicine; Miami, Florida, USADepartment of Surgery, Miami Transplant Institute, University of Miami Miller School of Medicine; Miami, Florida, USADepartment of Surgery, Miami Transplant Institute, University of Miami Miller School of Medicine; Miami, Florida, USADepartment of Surgery, Miami Transplant Institute, University of Miami Miller School of Medicine; Miami, Florida, USADepartment of Surgery, Miami Transplant Institute, University of Miami Miller School of Medicine; Miami, Florida, USAModern competing risks analysis has 2 primary goals in clinical epidemiology as follows: (i) to maximize the clinician’s knowledge of etiologic associations existing between potential predictor variables and various cause-specific outcomes via cause-specific hazard models, and (ii) to maximize the clinician’s knowledge of noteworthy differences existing in cause-specific patient risk via cause-specific subdistribution hazard models (cumulative incidence functions [CIFs]). A perfect application exists in analyzing the following 4 distinct outcomes after listing for a deceased donor kidney transplant (DDKT): (i) receiving a DDKT, (ii) receiving a living donor kidney transplant (LDKT), (iii) waitlist removal due to patient mortality or a deteriorating medical condition, and (iv) waitlist removal due to other reasons. It is important to realize that obtaining a complete understanding of subdistribution hazard ratios (HRs) is simply not possible without first having knowledge of the multivariable relationships existing between the potential predictor variables and the cause-specific hazards (perspective #1), because the cause-specific hazards form the “building blocks” of CIFs. In addition, though we believe that a worthy and practical alternative to estimating the median waiting-time-to DDKT is to ask, “what is the conditional probability of the patient receiving a DDKT, given that he or she would not previously experience one of the competing events (known as the cause-specific conditional failure probability),” only an appropriate estimator of this conditional type of cumulative incidence should be used (perspective #2). One suggested estimator, the well-known “one minus Kaplan-Meier” approach (censoring competing events), simply does not represent any probability in the presence of competing risks and will almost always produce biased estimates (thus, it should never be used).http://www.sciencedirect.com/science/article/pii/S2468024924000627cause-specific hazard ratescause-specific waiting time-to-event distributions following kidney transplant waitlistingconditional cumulative incidencecumulative incidencemodern competing risks analysis |
| spellingShingle | Jeffrey J. Gaynor Giselle Guerra Rodrigo Vianna Marina M. Tabbara Enric Lledo Graell Gaetano Ciancio Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical Perspectives Kidney International Reports cause-specific hazard rates cause-specific waiting time-to-event distributions following kidney transplant waitlisting conditional cumulative incidence cumulative incidence modern competing risks analysis |
| title | Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical Perspectives |
| title_full | Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical Perspectives |
| title_fullStr | Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical Perspectives |
| title_full_unstemmed | Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical Perspectives |
| title_short | Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical Perspectives |
| title_sort | competing risks analysis of kidney transplant waitlist outcomes two important statistical perspectives |
| topic | cause-specific hazard rates cause-specific waiting time-to-event distributions following kidney transplant waitlisting conditional cumulative incidence cumulative incidence modern competing risks analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2468024924000627 |
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