Graft survival after renal transplantation: agenda for analysis

Simple life-table analyses have not identified the reasons for the steady improvement over the years in graft survival. The use of more sophisticated multifactorial statistical techniques is indicated to identify prognostic factors and to explore the "center effect" on graft survival. An agenda for retrospective analysis of graft survival is proposed as follows. The first item is organizing and checking both survival and covariate data to provide a general description of the transplant series so that one knows, for example, which covariates are interdependent. Plotting lifetables and the time-specific risk of graft failure for one covariate at a time is the next step. The limitations of such analyses are easy to demonstrate and are illustrated with examples which motivate a multifactorial approach. Standard regression models for survival data, such as the exponential, Weibull, proportional hazards, and log-logistic, are described with the caveat that in medical applications, particularly the relevance of covariates, may be time-dependent, not constant on a particular scale (hazard or log odds). The method which is described for exploring how the relevance of covariates changes with elapsed time since transplantation is a simple step-function extension of the idea of proportional hazards. Because discrepancies in graft survival between centers, the "center effect," may owe something to the patient's rehabilitation before transplantation, a scoring system for rehabilitation or performance status would be useful as a summary covariate. Even careful retrospective analysis will not usually promote or indict a treatment policy without confirmation from randomized comparisons. A survey of renal units to elicit research priorities is suggested and factorial designs are recommended as an efficient way of investigating several specific questions. Multicenter collaboration in clinical trials would ensure that answers were speedily available.