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the data set may be incompletely documented. For example, patient followup of younger ESRD patients is not typically tracked in the Medicare data system until 90 days after first therapy for ESRD. Analyses of such incomplete data are susceptible to subtle forms of bias.

Patients with treated ESRD exhibit a wide variety of characteristics that influence mortality patterns. An evaluation of the importance of any one of these characteristics must also account for the potential impact on the results of the other characteristics.

The Medicare data collection system was designed primarily for reimbursement purposes and, consequently, has some limitations for research purposes. Some patient characteristics related to mortality, such as previous medical history, are not regularly recorded in it. Other characteristics, such as patient treatment history, are derived from billing records rather than from dedicated data collection instruments and, consequently, are subject to error.

Interpretation of the results of mortality analyses is complicated by the variety of analytical methods and types of numerical summaries that can be reported. Analytical methods include adjusted and unadjusted results, cross-tabulations and multiple regression models, parametric and nonparametric methods, Cox models and logistic regression models, and other methods discussed below. The results of statistical analyses can be summarized as death rates, death proportions, mortality ratios, and expected lifetimes.

An overview of several crucial issues central to the analysis of mortality data is presented in the first section as a series of questions. Each question is followed by some of the issues that should be addressed when answering the question. These issues recur in more specific forms in subsequent sections of the document.

In the second section, general strategies for adjusting statistical analyses for patient characteristics are discussed. Patient characteristics that are currently measured or that would be useful to measure are examined, and two approaches toward adjusting statistical analyses for patient characteristics are provided.

In the third section, several methods of survival analysis that are relevant to ESRD data are reviewed with the intent of showing how to compare, interpret, and synthesize the results of survival analyses. Most of the methods reviewed in this section have been used, or proposed for use, by other members of the renal research community. Each method has qualities that make it appropriate for specific purposes. Some proposals are also made in this section for analysis methods that have not yet been widely used in renal research. In addition, several different numerical parameters that are used to summarize the results of survival analyses are discussed.

The fourth section reviews several problems associated with the interpre-



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