On the relation between the cause‐specific hazard and the subdistribution rate for competing risks data: The Fine–Gray model revisited
The Fine–Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling is that the approach considers individuals still at risk for an event of cause 1 after they fell victim to the competing risk of cause 2. The subdistr...
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Published in | Biometrical journal Vol. 62; no. 3; pp. 790 - 807 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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Germany
Wiley - VCH Verlag GmbH & Co. KGaA
01.05.2020
John Wiley and Sons Inc |
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Abstract | The Fine–Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling is that the approach considers individuals still at risk for an event of cause 1 after they fell victim to the competing risk of cause 2. The subdistribution hazard and the extended risk sets, where subjects who failed of the competing risk remain in the risk set, are generally perceived as unnatural . One could say it is somewhat of a riddle why the Fine–Gray approach yields valid inference. To take away these uneasy feelings, we explore the link between the Fine–Gray and cause‐specific approaches in more detail. We introduce the reduction factor as representing the proportion of subjects in the Fine–Gray risk set that has not yet experienced a competing event. In the presence of covariates, the dependence of the reduction factor on a covariate gives information on how the effect of the covariate on the cause‐specific hazard and the subdistribution hazard relate. We discuss estimation and modeling of the reduction factor, and show how they can be used in various ways to estimate cumulative incidences, given the covariates. Methods are illustrated on data of the European Society for Blood and Marrow Transplantation. |
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AbstractList | The Fine-Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling is that the approach considers individuals still at risk for an event of cause 1 after they fell victim to the competing risk of cause 2. The subdistribution hazard and the extended risk sets, where subjects who failed of the competing risk remain in the risk set, are generally perceived as unnatural . One could say it is somewhat of a riddle why the Fine-Gray approach yields valid inference. To take away these uneasy feelings, we explore the link between the Fine-Gray and cause-specific approaches in more detail. We introduce the reduction factor as representing the proportion of subjects in the Fine-Gray risk set that has not yet experienced a competing event. In the presence of covariates, the dependence of the reduction factor on a covariate gives information on how the effect of the covariate on the cause-specific hazard and the subdistribution hazard relate. We discuss estimation and modeling of the reduction factor, and show how they can be used in various ways to estimate cumulative incidences, given the covariates. Methods are illustrated on data of the European Society for Blood and Marrow Transplantation. The Fine–Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling is that the approach considers individuals still at risk for an event of cause 1 after they fell victim to the competing risk of cause 2. The subdistribution hazard and the extended risk sets, where subjects who failed of the competing risk remain in the risk set, are generally perceived as unnatural . One could say it is somewhat of a riddle why the Fine–Gray approach yields valid inference. To take away these uneasy feelings, we explore the link between the Fine–Gray and cause‐specific approaches in more detail. We introduce the reduction factor as representing the proportion of subjects in the Fine–Gray risk set that has not yet experienced a competing event. In the presence of covariates, the dependence of the reduction factor on a covariate gives information on how the effect of the covariate on the cause‐specific hazard and the subdistribution hazard relate. We discuss estimation and modeling of the reduction factor, and show how they can be used in various ways to estimate cumulative incidences, given the covariates. Methods are illustrated on data of the European Society for Blood and Marrow Transplantation. Abstract The Fine–Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling is that the approach considers individuals still at risk for an event of cause 1 after they fell victim to the competing risk of cause 2. The subdistribution hazard and the extended risk sets, where subjects who failed of the competing risk remain in the risk set, are generally perceived as unnatural . One could say it is somewhat of a riddle why the Fine–Gray approach yields valid inference. To take away these uneasy feelings, we explore the link between the Fine–Gray and cause‐specific approaches in more detail. We introduce the reduction factor as representing the proportion of subjects in the Fine–Gray risk set that has not yet experienced a competing event. In the presence of covariates, the dependence of the reduction factor on a covariate gives information on how the effect of the covariate on the cause‐specific hazard and the subdistribution hazard relate. We discuss estimation and modeling of the reduction factor, and show how they can be used in various ways to estimate cumulative incidences, given the covariates. Methods are illustrated on data of the European Society for Blood and Marrow Transplantation. |
Author | Schumacher, Martin Putter, Hein van Houwelingen, Hans C. |
AuthorAffiliation | 1 Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands 2 Institute for Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg im Breisgau Germany |
AuthorAffiliation_xml | – name: 1 Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands – name: 2 Institute for Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg im Breisgau Germany |
Author_xml | – sequence: 1 givenname: Hein orcidid: 0000-0001-5395-1422 surname: Putter fullname: Putter, Hein email: h.putter@lumc.nl organization: Leiden University Medical Center – sequence: 2 givenname: Martin orcidid: 0000-0002-5037-0099 surname: Schumacher fullname: Schumacher, Martin organization: University of Freiburg – sequence: 3 givenname: Hans C. surname: van Houwelingen fullname: van Houwelingen, Hans C. organization: Leiden University Medical Center |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32128860$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1093/ije/dyr213 10.1007/978-1-4757-3294-8 10.1007/978-1-4612-4348-9 10.1002/sim.4385 10.1111/biom.12061 10.1002/sim.4780122406 10.1191/0962280202sm281ra 10.1201/b18695 10.1002/bimj.200900076 10.1002/sim.5773 10.1002/sim.3786 10.1002/sim.2600 10.1080/01621459.1999.10474144 10.2307/2530374 10.1002/sim.3844 10.1016/j.cmpb.2010.01.001 10.1002/sim.2712 |
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References | 2010; 99 1993; 12 2013b; 69 2012 1978; 34 2011 2013; 32 2000 2010; 29 2013a; 29 2002; 11 1993 2015 2014 2013 1999; 94 2010; 52 2012; 31 2012; 41 2007; 26 e_1_2_9_20_1 Beyersmann J. (e_1_2_9_6_1) 2011 Beyersmann J. (e_1_2_9_7_1) 2013 Nicolaie M. A. (e_1_2_9_16_1) 2013; 29 e_1_2_9_11_1 e_1_2_9_22_1 e_1_2_9_10_1 e_1_2_9_21_1 e_1_2_9_12_1 Gray B. (e_1_2_9_13_1) 2014 e_1_2_9_8_1 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_2_1 e_1_2_9_9_1 e_1_2_9_15_1 e_1_2_9_17_1 Houwelingen H. (e_1_2_9_14_1) 2012 e_1_2_9_19_1 e_1_2_9_18_1 |
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Snippet | The Fine–Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling is that... The Fine-Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling is that... Abstract The Fine–Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling... |
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SubjectTerms | cause‐specific hazard competing risks cumulative incidence Hazards proportional hazards Reduction Research Paper Research Papers Risk perception subdistribution hazard Transplantation |
Title | On the relation between the cause‐specific hazard and the subdistribution rate for competing risks data: The Fine–Gray model revisited |
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