Intermediate test results often occur with diagnostic tests. When assessing diagnostic accuracy, it is important to properly report and account for these results. In the literature, these results are commonly discarded prior to analysis or treated as either a positive or a negative result. Although such adjustments allow sensitivity and specificity to be computed in the standard way, these forced decisions limit the interpretability and usefulness of the results. Estimation of diagnostic accuracy is further complicated when tests are evaluated without a gold standard. Although traditional latent class modeling can be readily applied to analyze these data and account for intermediate results, these models assume that tests are independent conditional on the true disease status, which is rarely valid in practice. We extend both the log-linear latent class model and the probit latent class model to accommodate the conditional dependence among tests while taking the intermediate results into consideration. We illustrate our methods using a simulation study and a published medical study on the detection of epileptiform activity in the brain.

译文

诊断测试通常会出现中间测试结果。在评估诊断准确性时,正确报告和说明这些结果很重要。在文献中,这些结果通常在分析之前被丢弃,或者被视为阳性或阴性结果。尽管此类调整允许以标准方式计算敏感性和特异性,但这些强制性决定限制了结果的可解释性和实用性。如果在没有黄金标准的情况下对测试进行评估,则诊断准确性的估计会更加复杂。尽管传统的潜在类别建模可以轻松地用于分析这些数据并说明中间结果,但是这些模型假定测试是基于真实疾病状态的独立条件,在实践中很少有效。我们扩展了对数线性潜在类模型和概率潜在类模型,以适应测试之间的条件依赖性,同时考虑了中间结果。我们使用模拟研究和已发表的医学研究来说明我们的方法,以检测大脑中的癫痫样活动。

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