Statistical Methods for Cohort Studies of CKD: Survival Analysis in the Setting of Competing Risks

Clinical journal of the American Society of Nephrology: CJASN

Jesse Yenchih Hsu² 

[1] *Department of Biostatistics and Epidemiology and..;*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..*Department of Biostatistics and Epidemiology and..‡Department of Medicine, School of Medicine, Duke University, Durham, North Carolina;§Division of Nephrology and Hypertension, Department of Medicine, Northwestern University, Chicago, Illinois;¶Division of Nephrology and Hypertension, Case Western Reserve University, Cleveland, Ohio

关键词: Cumulative incidence function;    Cause-specific;    Cox proportional hazards models;    FGF-23;    Cardiovascular Diseases;    Fibroblast Growth Factors;    Follow-Up Studies;    Kidney Failure;    Chronic;    Proportional Hazards Models;    Renal Insufficiency;    Chronic;    Risk;    Survival Analysis;    Fibroblast growth factor 23;   

DOI  :  10.2215/CJN.10301016

学科分类：泌尿医学

来源: American Society of Nephrology

【 摘 要 】

Survival analysis is commonly used to evaluate factors associated with time to an event of interest (e.g., ESRD, cardiovascular disease, and mortality) among CKD populations. Time to the event of interest is typically observed only for some participants. Other participants have their event time censored because of the end of the study, death, withdrawal from the study, or some other competing event. Classic survival analysis methods, such as Cox proportional hazards regression, rely on the assumption that any censoring is independent of the event of interest. However, in most clinical settings, such as in CKD populations, this assumption is unlikely to be true. For example, participants whose follow-up time is censored because of health-related death likely would have had a shorter time to ESRD, had they not died. These types of competing events that cause dependent censoring are referred to as competing risks. Here, we first describe common circumstances in clinical renal research where competing risks operate and then review statistical approaches for dealing with competing risks. We compare two of the most popular analytical methods used in settings of competing risks: cause-specific hazards models and the Fine and Gray approach (subdistribution hazards models). We also discuss practical recommendations for analysis and interpretation of survival data that incorporate competing risks. To demonstrate each of the analytical tools, we use a study of fibroblast growth factor 23 and risks of mortality and ESRD in participants with CKD from the Chronic Renal Insufficiency Cohort Study.

【 授权许可】

CC BY