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

Jesse Yenchih Hsu, Jason A. Roy, Dawei Xie, Wei Yang, Haochang Shou, Amanda Hyre Anderson, J. Richard Landis, Christopher Jepson, Myles Wolf, Tamara Isakova, Mahboob Rahman and Harold I. Feldman; on behalf of the Chronic Renal Insufficiency Cohort (CRIC) Study Investigators
CJASN July 2017, 12 (7) 1181-1189; DOI: https://doi.org/10.2215/CJN.10301016
Jesse Yenchih Hsu
*Department of Biostatistics and Epidemiology and
†Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;
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Jason A. Roy
*Department of Biostatistics and Epidemiology and
†Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;
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Dawei Xie
*Department of Biostatistics and Epidemiology and
†Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;
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Wei Yang
*Department of Biostatistics and Epidemiology and
†Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;
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Haochang Shou
*Department of Biostatistics and Epidemiology and
†Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;
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Amanda Hyre Anderson
*Department of Biostatistics and Epidemiology and
†Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;
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J. Richard Landis
*Department of Biostatistics and Epidemiology and
†Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;
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Christopher Jepson
*Department of Biostatistics and Epidemiology and
†Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;
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Myles Wolf
‡Department of Medicine, School of Medicine, Duke University, Durham, North Carolina;
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Tamara Isakova
§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
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Mahboob Rahman
¶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
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Harold I. Feldman
*Department of Biostatistics and Epidemiology and
†Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania;
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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.

  • 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
  • Copyright © 2017 by the American Society of Nephrology
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Clinical Journal of the American Society of Nephrology: 12 (7)
Clinical Journal of the American Society of Nephrology
Vol. 12, Issue 7
July 07, 2017
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Statistical Methods for Cohort Studies of CKD: Survival Analysis in the Setting of Competing Risks
Jesse Yenchih Hsu, Jason A. Roy, Dawei Xie, Wei Yang, Haochang Shou, Amanda Hyre Anderson, J. Richard Landis, Christopher Jepson, Myles Wolf, Tamara Isakova, Mahboob Rahman, Harold I. Feldman
CJASN Jul 2017, 12 (7) 1181-1189; DOI: 10.2215/CJN.10301016

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Statistical Methods for Cohort Studies of CKD: Survival Analysis in the Setting of Competing Risks
Jesse Yenchih Hsu, Jason A. Roy, Dawei Xie, Wei Yang, Haochang Shou, Amanda Hyre Anderson, J. Richard Landis, Christopher Jepson, Myles Wolf, Tamara Isakova, Mahboob Rahman, Harold I. Feldman
CJASN Jul 2017, 12 (7) 1181-1189; DOI: 10.2215/CJN.10301016
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  • Article
    • Abstract
    • Introduction
    • Motivating Example: Is Fibroblast Growth Factor 23 Associated with Time to ESRD?
    • Introduction to Classic Survival Analysis for a Single Event
    • Independent Censoring and Competing Events
    • Analytical Strategies and Key Assumptions in the Presence of a Competing Event
    • A Motivating Example in the Presence of Competing and Independent Censoring Events
    • Discussion
    • Disclosures
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data Supps
  • Info & Metrics
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  • Competing mortality risks analysis of prediagnostic lifestyle and dietary factors in colorectal cancer survival: the Norwegian Women and Cancer Study
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Keywords

  • 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
  • Risk
  • Survival Analysis
  • fibroblast growth factor 23

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