title: Statistical Methods for Cohort Studies of CKD: Survival Analysis in the Setting of Competing Risks.
abstract: 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.
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Jesse Yenchih
authors.initials: JY
authors.lastname: Hsu
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Jason A
authors.initials: JA
authors.lastname: Roy
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Dawei
authors.initials: D
authors.lastname: Xie
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Haochang
authors.initials: H
authors.lastname: Shou
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Amanda Hyre
authors.initials: AH
authors.lastname: Anderson
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: J Richard
authors.initials: JR
authors.lastname: Landis
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Christopher
authors.initials: C
authors.lastname: Jepson
authors.affiliation: Department of Medicine, School of Medicine, Duke University, Durham, North Carolina.
authors.forename: Myles
authors.initials: M
authors.lastname: Wolf
authors.affiliation: Division of Nephrology and Hypertension, Department of Medicine, Northwestern University, Chicago, Illinois.|Center for Translational Metabolism and Health, Northwestern University Feinberg School of Medicine, Chicago, Illinois; and.
authors.forename: Tamara
authors.initials: T
authors.lastname: Isakova
authors.affiliation: Division of Nephrology and Hypertension, Case Western Reserve University, Cleveland, Ohio.|University Hospitals Cleveland Medical Center, Cleveland, Ohio; and.|Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio.
authors.forename: Mahboob
authors.initials: M
authors.lastname: Rahman
authors.affiliation: Department of Biostatistics and Epidemiology and.|Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Harold I
authors.initials: HI
authors.lastname: Feldman
mesh_terms: D015415:Biomarkers / Q000097:blood; D002423:Cause of Death; D018450:Disease Progression; D000089703:Fibroblast Growth Factor-23; D005346:Fibroblast Growth Factors / Q000097:blood; D006801:Humans; D053208:Kaplan-Meier Estimate; D007676:Kidney Failure, Chronic / Q000097:blood / Q000175:diagnosis / Q000401:mortality* / Q000628:therapy; D015233:Models, Statistical; D011379:Prognosis; D016016:Proportional Hazards Models; D051436:Renal Insufficiency, Chronic / Q000097:blood / Q000175:diagnosis / Q000401:mortality* / Q000628:therapy; D018570:Risk Assessment; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors
chemical_list: D015415:Biomarkers; C000717427:FGF23 protein, human; D005346:Fibroblast Growth Factors; D000089703:Fibroblast Growth Factor-23
keywords: Cardiovascular Diseases; Cause-specific; Chronic; Cox proportional hazards models; Cumulative incidence function; FGF-23; Fibroblast Growth Factors; Fibroblast growth factor 23; Follow-Up Studies; Kidney Failure; Proportional Hazards Models; Renal Insufficiency; Risk; Survival Analysis