In many healthcare and social science applications, information about units is dispersed across multiple data files. Linking records across files is necessary to estimate the associations of interest. Common record linkage algorithms only rely on similarities between linking variables that appear in all the files. Moreover, analysis of linked files often ignores errors that may arise from incorrect or missed links. Bayesian record linking methods allow for natural propagation of linkage error, by jointly sampling the linkage structure and the model parameters. We extend an existing Bayesian record linkage method to integrate associations between variables exclusive to each file being linked. We show analytically, and using simulations, that this method can improve the linking process, and can yield accurate inferences. We apply the method to link Meals on Wheels recipients to Medicare Enrollment records.

翻译：摘要：在许多医疗健康和社会科学应用中，个体信息分散于多个数据文件中。跨文件记录链接是估计目标关联的必要手段。传统记录链接算法仅依赖各文件中共有的链接变量相似度，且链接文件的分析常忽略错误或遗漏链接可能产生的误差。贝叶斯记录链接方法通过联合采样链接结构与模型参数，自然实现了链接误差的传播。我们扩展了一种现有贝叶斯记录链接方法，整合了各链接文件中特有变量间的关联。通过理论推导与模拟实验证明，该方法能优化链接过程并产生准确的推断结果。我们将该方法应用于“流动餐车”受益者与医疗保险登记记录的链接。