Initial data analysis of the national German transplantation registry with a focus on kidney transplantation

TL;DR

This paper conducts an initial analysis of the German Transplantation Registry data, focusing on kidney transplants, to understand data quality, missingness, and inform future research efforts.

Contribution

It provides a comprehensive assessment of data quality, missing data patterns, and variable discrepancies in the German Transplantation Registry, offering recommendations for future analyses.

Findings

Missing data proportions vary widely across tables.

Decision tree approach effectively identifies missing data patterns.

Identified variables suitable for imputing missing data.

Abstract

This study presents an Initial Data Analysis (IDA) of the German Transplantation Registry (TxReg) data for a better data understanding and to inform future data analyses. The IDA is focusing on data on first-time kidney-only transplantations in adult recipients from deceased donors between 2006 and 2016 and refers to data from 14,954 recipients and 9,964 donors across 25 tables. Investigated aspects include missing data patterns and structure, data consistency, and availability of event time data. Results show that missing data proportions vary widely, with some tables nearly complete while others have over 50% missing values. Missing data patterns are identified using a decision tree approach. An influx and outflux analysis demonstrates that some variables have high potential for imputing missing data, while others were less suitable for imputation. We identified 168 multi-sourced variables that are reported by multiple data providers in parallel leading to discrepancies for some variables but also providing opportunities for missing data imputation. Our findings on event time data demonstrate the importance of carefully selecting the variables used for event time analyses as results will strongly depend on this selection. In summary, our findings highlight the challenges when utilizing the TxReg data for research and provide recommendations for data preprocessing and analysis in future analyses.