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Considerations in the Statistical Analysis of Hemodialysis Patient Survival

Christos Argyropoulos^*, Chung-Chou H. Chang^,, Laura Plantinga, Nancy Fink^,||, Neil Powe^,||,¶ and Mark Unruh^*

*Renal-Electrolyte Division, University of Pittsburgh Medical Center, and
General Internal Medicine Division, School of Medicine, and
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania; and
Departments of ^||Medicine,
Epidemiology, and
^¶Health Policy Management, Johns Hopkins University, Baltimore, Maryland

Correspondence: Dr. Christos Argyropoulos, University of Pittsburgh Medical Center, Renal-Electrolyte Division, 3550 Terrace Street, A919 Scaife Hall, Pittsburgh, PA 15261. Phone: 412-647-8394; Fax: 412-647-6222; E-mail: argyropoulosc{at}upmc.edu

Received for publication May 28, 2008. Accepted for publication May 5, 2009.

The association of hemodialysis dosage with patient survival is controversial. Here, we tested the hypothesis that methods for survival analysis may influence conclusions regarding dialysis dosage and mortality. We analyzed all-cause mortality by proportional hazards and accelerated failure time regression models in a cohort of incident hemodialysis patients who were followed for 9 yr. Both models identified age, race, heart failure, physical functioning, and comorbidity scores as important predictors of patient survival. Using proportional hazards, there was no statistically significant association between mortality and Kt/V (hazard ratio 0.72; 95% confidence interval 0.45 to 1.14). In contrast, using accelerated failure time models, each 0.1-U increment of Kt/V improved adjusted median patient survival by 3.50% (95% confidence interval 0.20 to 7.08%). Proportional hazard models also yielded less accurate estimates for median survival. These findings are consistent with an additive damage model for the survival of patients who are on hemodialysis. In this conceptual model, the assumptions of the proportional hazard model are violated, leading to underestimation of the importance of dialysis dosage. These results suggest that future studies of dialysis adequacy should consider this additive damage model when selecting methods for survival analysis. Accelerated failure time models may be useful adjuncts to the Cox model when studying outcomes of dialysis patients.

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Copyright © 2009 by the American Society of Nephrology. Online ISSN: 1533-3450 Print ISSN: 1046-6673