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首页> 外文期刊>Journal of statistical computation and simulation >Bayesian comparison of diagnostic tests with largely non-informative missing data
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Bayesian comparison of diagnostic tests with largely non-informative missing data

机译：贝叶斯诊断测试与大部分非信息缺失数据的诊断测试

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摘要

This work was motivated by a real problem of comparing binary diagnostic tests based upon a gold standard, where the collected data showed that the large majority of classifications were incomplete and the feedback received from the medical doctors allowed us to consider the missingness as non-informative. Taking into account the degree of data incompleteness, we used a Bayesian approach via MCMC methods for drawing inferences of interest on accuracy measures. Its direct implementation by well-known software demonstrated serious problems of chain convergence. The difficulties were overcome by the proposal of a simple, efficient and easily adaptable data augmentation algorithm, performed through an ad hoc computer program.
机译：这项工作是由基于黄金标准进行比较二元诊断测试的真正问题，其中收集的数据显示，大多数分类是不完整的，并且从医学医生收到的反馈让我们将失踪视为非信息性。考虑到数据不完整的程度，我们通过MCMC方法使用了贝叶斯方法，用于吸引对准确度措施的兴趣推断。其通过着名的软件直接实施表现出严重的链接趋同问题。通过Ad Hoc计算机程序执行简单，高效且易于适应性的数据增强算法的提议，克服了困难。

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