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首页> 外文期刊>Journal of applied statistics >A fast Monte Carlo expectation-maximization algorithm for estimation in latent class model analysis with an application to assess diagnostic accuracy for cervical neoplasia in women with atypical glandular cells
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A fast Monte Carlo expectation-maximization algorithm for estimation in latent class model analysis with an application to assess diagnostic accuracy for cervical neoplasia in women with atypical glandular cells

机译：一种快速的蒙特卡洛期望最大化算法，用于潜在类模型分析中的估计，可用于评估非典型腺细胞女性宫颈癌的诊断准确性

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摘要

In this article, we use a latent class model (LCM) with prevalence modeled as a function of covariates to assess diagnostic test accuracy in situations where the true disease status is not observed, but observations on three or more conditionally independent diagnostic tests are available. A fast Monte Carlo expectation-maximization (MCEM) algorithm with binary (disease) diagnostic data is implemented to estimate parameters of interest; namely, sensitivity, specificity, and prevalence of the disease as a function of covariates. To obtain standard errors for confidence interval construction of estimated parameters, the missing information principle is applied to adjust information matrix estimates. We compare the adjusted information matrix-based standard error estimates with the bootstrap standard error estimates both obtained using the fast MCEM algorithm through an extensive Monte Carlo study. Simulation demonstrates that the adjusted information matrix approach estimates the standard error similarly with the bootstrap methods under certain scenarios. The bootstrap percentile intervals have satisfactory coverage probabilities. We then apply the LCM analysis to a real data set of 122 subjects from a Gynecologic Oncology Group study of significant cervical lesion diagnosis in women with atypical glandular cells of undetermined significance to compare the diagnostic accuracy of a histology-based evaluation, a carbonic anhydrase-IX biomarker-based test and a human papillomavirus DNA test.
机译：在本文中，我们使用潜伏类模型（LCM），并将患病率建模为协变量的函数，以在未观察到真实疾病状态，但可以观察到三个或三个以上条件独立诊断测试的情况下评估诊断测试的准确性。一种带有二进制（疾病）诊断数据的快速蒙特卡洛期望最大化（MCEM）算法可用于估计目标参数。也就是说，疾病的敏感性，特异性和患病率是协变量的函数。为了获得估计参数的置信区间构建的标准误差，将缺失信息原理应用于调整信息矩阵估计。我们将经过调整的基于信息矩阵的标准误差估计值与引导程序标准误差估计值进行比较，两者均使用快速MCEM算法通过广泛的蒙特卡洛研究获得。仿真表明，在某些情况下，调整后的信息矩阵方法与自举方法相似，可以估算标准误差。自举百分比间隔具有令人满意的覆盖概率。然后，我们将LCM分析应用于妇科肿瘤学小组对122例受试者的真实数据进行的研究，该研究对具有非典型意义的非典型腺细胞的女性进行重大宫颈病变诊断，以比较基于组织学评估，碳酸酐酶- IX基于生物标记的测试和人乳头瘤病毒DNA测试。

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