Transplant data: sources, collection and research considerations, 2004.

The process of collecting and analyzing transplant data is complex. Familiarity with how these data are collected is crucial to a thorough understanding of the information. This article focuses on available OPTN-SRTR data and the continuing evolution of data collection mechanisms; how that data collection system is improving the data quality and reducing the data collection burden; how additional ascertainment of outcomes both completes and validates existing data; and caveats that remain for researchers. This year's article focuses further on research considerations related to cohort choice, timing of data submission, and potential biases in follow-up data. Ongoing improvements in data collection timeliness and scope are covered. The impact of extra ascertainment of outcomes, particularly for post-transplant kidney graft failure from Medicare data, are also examined. A section on graft failure reporting among different sources traces the steps by which the SRTR reconciles different data sources in its analyses. It is important that those reading and conducting transplant research understand the origin, structure, and scope of the available data. All of these issues should be carefully considered when choosing cohorts and data sources for analysis.

Analytical approaches for transplant research, 2004.

This article provides detailed explanations of the methods frequently employed in outcomes analyses performed by the Scientific Registry of Transplant Recipients (SRTR). All aspects of the analytical process are discussed, including cohort selection, post-transplant follow-up analysis, outcome definition, ascertainment of events, censoring, and adjustments. The methods employed for descriptive analyses are described, such as unadjusted mortality rates and survival probabilities, and the estimation of covariant effects through regression modeling. A section on transplant waiting time focuses on the kidney and liver waiting lists, pointing out the different considerations each list requires and the larger questions that such analyses raise. Additionally, this article describes specialized modeling strategies recently designed by the SRTR and aimed at specific organ allocation issues. The article concludes with a description of simulated allocation modeling (SAM), which has been developed by the SRTR for three organ systems: liver, thoracic organs, and kidney-pancreas. SAMs are particularly useful for comparing outcomes for proposed national allocation policies. The use of SAMs has already helped in the development and implementation of a new policy for liver candidates with high MELD scores to be offered organs regionally before the organs are offered to candidates with low MELD scores locally.

Transplant data: sources, collection, and caveats.

By examining the sources, quality and organization of transplant data available, as well as making observations about data reporting patterns and accuracy, we hope to improve understanding of existing results, help researchers with study design and stimulate new exploratory initiatives. The primary data source, collected by the OPTN, has benefited from extensive recent technological advances. Transplant professionals now report patient and donor data more easily, quickly, and accurately, improving data timeliness and precision. Secondary sources may be incorporated, improving the accuracy and expanding the scope of analyses. For example, auxiliary mortality data allows more accurate survival analysis and conclusions regarding the completeness of center-reported post-transplant follow-up. Furthermore, such sources enable examination of outcomes not reported by centers, such as mortality after waiting list removal, providing more appropriate comparisons of waiting list and post-transplant mortality. Complex collection and reporting processes require specific analytical methods and may lead to potential pitfalls. Patterns in the timing of reporting adverse events differ from those for 'positive' events, yielding the need for care in choosing cohorts and censor dates to avoid bias. These choices are further complicated by the use of multiple sources of data, with different time lags and reporting patterns.

Analytical approaches for transplant research.

It is highly desirable to base decisions designed to improve medical practice or organ allocation policies on the analyses of the most recent data available. Yet there is often a need to balance this desire with the added value of evaluating long-term outcomes (e.g. 5-year mortality rates), which requires the use of data from earlier years. This article explains the methods used by the Scientific Registry of Transplant Recipients in order to achieve these goals simultaneously. The analysis of waiting list and transplant outcomes depends strongly on statistical methods that can combine data from different cohorts of patients that have been followed for different lengths of time. A variety of statistical methods have been designed to address these goals, including the Kaplan-Meier estimator, Cox regression models, and Poisson regression. An in-depth description of the statistical methods used for calculating waiting times associated with the various types of organ transplants is provided. Risk of mortality and graft failure, adjusted analyses, cohort selection, and the many complicating factors surrounding the calculation of follow-up time for various outcomes analyses are also examined.