%0 Journal Article %1 Latouche:2013:J-Clin-Epidemiol:23415868 %A Latouche, A %A Allignol, A %A Beyersmann, J %A Labopin, M %A Fine, J P %D 2013 %J J Clin Epidemiol %K CompetingRisks SurvivalAnalysis review statistics %N 6 %P 648-653 %R 10.1016/j.jclinepi.2012.09.017 %T A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions %U https://www.ncbi.nlm.nih.gov/pubmed/23415868 %V 66 %X Competing risks endpoints are frequently encountered in hematopoietic stem cell transplantation where patients are exposed to relapse and treatment-related mortality. Both cause-specific hazards and direct models for the cumulative incidence functions have been used for analyzing such competing risks endpoints. For both approaches, the popular models are of a proportional hazards type. Such models have been used for studying prognostic factors in acute and chronic leukemias. We argue that a complete understanding of the event dynamics requires that both hazards and cumulative incidence be analyzed side by side, and that this is generally the most rigorous scientific approach to analyzing competing risks data. That is, understanding the effects of covariates on cause-specific hazards and cumulative incidence functions go hand in hand. A case study illustrates our proposal.

@article{Latouche:2013:J-Clin-Epidemiol:23415868, abstract = {Competing risks endpoints are frequently encountered in hematopoietic stem cell transplantation where patients are exposed to relapse and treatment-related mortality. Both cause-specific hazards and direct models for the cumulative incidence functions have been used for analyzing such competing risks endpoints. For both approaches, the popular models are of a proportional hazards type. Such models have been used for studying prognostic factors in acute and chronic leukemias. We argue that a complete understanding of the event dynamics requires that both hazards and cumulative incidence be analyzed side by side, and that this is generally the most rigorous scientific approach to analyzing competing risks data. That is, understanding the effects of covariates on cause-specific hazards and cumulative incidence functions go hand in hand. A case study illustrates our proposal.}, added-at = {2019-11-18T10:05:10.000+0100}, author = {Latouche, A and Allignol, A and Beyersmann, J and Labopin, M and Fine, J P}, biburl = {https://www.bibsonomy.org/bibtex/23bbb5b059b0840467c8750e0a4cfd6d5/jkd}, description = {A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions. - PubMed - NCBI}, doi = {10.1016/j.jclinepi.2012.09.017}, interhash = {bdcc6be52b94319afcac6d7b4dacd609}, intrahash = {3bbb5b059b0840467c8750e0a4cfd6d5}, journal = {J Clin Epidemiol}, keywords = {CompetingRisks SurvivalAnalysis review statistics}, month = jun, number = 6, pages = {648-653}, pmid = {23415868}, timestamp = {2019-11-18T10:05:10.000+0100}, title = {A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions}, url = {https://www.ncbi.nlm.nih.gov/pubmed/23415868}, volume = 66, year = 2013 }