%0 Journal Article %1 deGlas:2016:J-Natl-Cancer-Inst:26614095 %A de Glas, N A %A Kiderlen, M %A Vandenbroucke, J P %A de Craen, A J %A Portielje, J E %A van de Velde, C J %A Liefers, G J %A Bastiaannet, E %A Le Cessie, S %D 2016 %J J Natl Cancer Inst %K CompetingRisks SurvivalAnalysis oncology statistics %N 5 %R 10.1093/jnci/djv366 %T Performing Survival Analyses in the Presence of Competing Risks: A Clinical Example in Older Breast Cancer Patients %U https://www.ncbi.nlm.nih.gov/pubmed/26614095 %V 108 %X An important consideration in studies that use cause-specific endpoints such as cancer-specific survival or disease recurrence is that risk of dying from another cause before experiencing the event of interest is generally much higher in older patients. Such competing events are of major importance in the design and analysis of studies with older patients, as a patient who dies from another cause before the event of interest cannot reach the endpoint. In this Commentary, we present several clinical examples of research questions in a population-based cohort of older breast cancer patients with a high frequency of competing events and discuss implications of choosing models that deal with competing risks in different ways. We show that in populations with high frequency of competing events, it is important to consider which method is most appropriate to estimate cause-specific endpoints. We demonstrate that when calculating absolute cause-specific risks the Kaplan-Meier method overestimates risk of the event of interest and that the cumulative incidence competing risks (CICR) method, which takes competing risks into account, should be used instead. Two approaches are commonly used to model the association between prognostic factors and cause-specific survival: the Cox proportional hazards model and the Fine and Gray model. We discuss both models and show that in etiologic research the Cox Proportional Hazards model is recommended, while in predictive research the Fine and Gray model is often more appropriate. In conclusion, in studies with cause-specific endpoints in populations with a high frequency of competing events, researchers should carefully choose the most appropriate statistical method to prevent incorrect interpretation of results.

@article{deGlas:2016:J-Natl-Cancer-Inst:26614095, abstract = {An important consideration in studies that use cause-specific endpoints such as cancer-specific survival or disease recurrence is that risk of dying from another cause before experiencing the event of interest is generally much higher in older patients. Such competing events are of major importance in the design and analysis of studies with older patients, as a patient who dies from another cause before the event of interest cannot reach the endpoint. In this Commentary, we present several clinical examples of research questions in a population-based cohort of older breast cancer patients with a high frequency of competing events and discuss implications of choosing models that deal with competing risks in different ways. We show that in populations with high frequency of competing events, it is important to consider which method is most appropriate to estimate cause-specific endpoints. We demonstrate that when calculating absolute cause-specific risks the Kaplan-Meier method overestimates risk of the event of interest and that the cumulative incidence competing risks (CICR) method, which takes competing risks into account, should be used instead. Two approaches are commonly used to model the association between prognostic factors and cause-specific survival: the Cox proportional hazards model and the Fine and Gray model. We discuss both models and show that in etiologic research the Cox Proportional Hazards model is recommended, while in predictive research the Fine and Gray model is often more appropriate. In conclusion, in studies with cause-specific endpoints in populations with a high frequency of competing events, researchers should carefully choose the most appropriate statistical method to prevent incorrect interpretation of results. }, added-at = {2019-10-27T09:42:13.000+0100}, author = {de Glas, N A and Kiderlen, M and Vandenbroucke, J P and de Craen, A J and Portielje, J E and van de Velde, C J and Liefers, G J and Bastiaannet, E and Le Cessie, S}, biburl = {https://www.bibsonomy.org/bibtex/2403cb0295a5700711e0f8e2fe79169ff/jkd}, description = {Performing Survival Analyses in the Presence of Competing Risks: A Clinical Example in Older Breast Cancer Patients. - PubMed - NCBI}, doi = {10.1093/jnci/djv366}, interhash = {37468e03fba383897afdf3424f539c88}, intrahash = {403cb0295a5700711e0f8e2fe79169ff}, journal = {J Natl Cancer Inst}, keywords = {CompetingRisks SurvivalAnalysis oncology statistics}, month = may, number = 5, pmid = {26614095}, timestamp = {2019-10-27T09:42:13.000+0100}, title = {Performing Survival Analyses in the Presence of Competing Risks: A Clinical Example in Older Breast Cancer Patients}, url = {https://www.ncbi.nlm.nih.gov/pubmed/26614095}, volume = 108, year = 2016 }