title: Preservation of glomerular filtration rate on dialysis when adjusted for patient dropout.
abstract: BACKGROUND
  Residual renal function (RRF) plays an important role in dialysis patients. Studies in patients on maintenance dialysis suggest that RRF is better preserved in patients receiving peritoneal dialysis (PD) vis-à-vis those receiving hemodialysis (HD). We speculated that regardless of the patient's type of therapy, the estimate obtained for the rate of decline in glomerular filtration rate (GFR) may be biased because of informative censoring associated with patient dropout. Informative censoring occurs when patients who die or transfer to another modality very early have associated with them a lower starting GFR or a higher rate of decline of GFR than patients who either complete the study or who die or transfer much later. If patient dropout is indeed related to the rate of decline in GFR and if this relationship is ignored in the analysis, then the estimate obtained of the rate of decline in GFR may be biased.
  
  
  METHODS
  In an attempt to determine if there is a relationship between patient dropout and the decline in GFR, we reanalyzed the CANUSA data by modeling GFR as a nonlinear function of time with the rate of decline being exponential.
  
  
  RESULTS
  This article highlights the significance of "informative censoring" when studying the decline of RRF on dialysis. The results show that for the CANUSA cohort, the mean initial GFR was significantly lower, and the rate of decline was significantly higher for patients who died or transferred to HD than for patients who were randomly censored or received a transplant. It is important to emphasize that the impact of informative censoring on previous analyses of the decline of RRF between PD versus HD is presently unclear. If bias caused by informative censoring is the same regardless of what therapy a patient is on, then conclusions from previous studies comparing the decline in GFR between PD and HD would still be valid. However, if the magnitude of the bias differs according to therapy, then additional adjustments would be needed to fairly compare the decline in GFR between PD and HD. Because this analysis is restricted to patients on PD, it would be scientifically incorrect to interpret previous studies solely on the basis of the results from this analysis.
  
  
  CONCLUSIONS
  In any longitudinal study designed to estimate trends in an outcome measured over time, it is important that the analysis of the data takes into account any effect patient dropout may have on the estimated trend. This analysis demonstrates that among PD patients, both the starting GFR and the rate of decline in GFR are associated with patient dropout. Consequently, future studies aimed at estimating the rate of decline in GFR among PD patients should also account for any dependencies between dropout and GFR. Similarly, data analyzing for apparent differences in the rate of decline of GFR between PD and HD should also adjust for possible informative censoring.
authors.affiliation: Division of Nephrology, Department of Internal Medicine, and Dalton Cardiovascular Research Center, University ofMissouri-Columbia, and Dialysis Clinic, Inc., Columbia, Missouri 65212, USA.
authors.forename: M
authors.initials: M
authors.lastname: Misra
authors.forename: E
authors.initials: E
authors.lastname: Vonesh
authors.forename: D N
authors.initials: DN
authors.lastname: Churchill
authors.forename: H L
authors.initials: HL
authors.lastname: Moore
authors.forename: J C
authors.initials: JC
authors.lastname: Van Stone
authors.forename: K D
authors.initials: KD
authors.lastname: Nolph
mesh_terms: D005919:Glomerular Filtration Rate; D006801:Humans; D007668:Kidney / Q000502:physiology; D007676:Kidney Failure, Chronic / Q000401:mortality / Q000503:physiopathology* / Q000628:therapy*; D008137:Longitudinal Studies; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D010530:Peritoneal Dialysis / Q000706:statistics & numerical data*; D006435:Renal Dialysis / Q000706:statistics & numerical data*