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Titolo	Considerations in the Statistical Analysis of Hemodialysis Patient Survival
Autore	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
Referenza	J Am Soc Nephrol 2009; doi: 10.1681/ASN.2008050551
Contenuto	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.
Data	10.08.2009

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