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Hidden mover-stayer model for disease progression accounting for misclassified and partially observed diagnostic tests: Application to the natural history of human papillomavirus and cervical precancer
Statistics in Medicine ( IF 1.8 ) Pub Date : 2021-04-12 , DOI: 10.1002/sim.8977
Jordan Aron 1 , Paul S Albert 1 , Nicolas Wentzensen 2 , Li C Cheung 1
Affiliation  

Hidden Markov models (HMMs) have been proposed to model the natural history of diseases while accounting for misclassification in state identification. We introduce a discrete time HMM for human papillomavirus (HPV) and cervical precancer/cancer where the hidden and observed state spaces are defined by all possible combinations of HPV, cytology, and colposcopy results. Because the population of women undergoing cervical cancer screening is heterogeneous with respect to sexual behavior, and therefore risk of HPV acquisition and subsequent precancers, we use a mover-stayer mixture model that assumes a proportion of the population will stay in the healthy state and are not subject to disease progression. As each state is a combination of three distinct tests that characterize the cervix, partially observed data arise when at least one but not every test is observed. The standard forward-backward algorithm, used for evaluating the E-step within the E-M algorithm for maximum-likelihood estimation of HMMs, cannot incorporate time points with partially observed data. We propose a new forward-backward algorithm that considers all possible fully observed states that could have occurred across a participant's follow-up visits. We apply our method to data from a large management trial for women with low-grade cervical abnormalities. Our simulation study found that our method has relatively little bias and out preforms simpler methods that resulted in larger bias.

中文翻译:

用于解释错误分类和部分观察到的诊断测试的疾病进展的隐藏移动模型:应用于人乳头瘤病毒和宫颈癌前病变的自然史

已提出隐马尔可夫模型 (HMM) 来模拟疾病的自然历史,同时考虑状态识别中的错误分类。我们为人乳头瘤病毒 (HPV) 和宫颈癌前病变/癌症引入了离散时间 HMM,其中隐藏和观察到的状态空间由 HPV、细胞学和阴道镜检查结果的所有可能组合定义。由于接受宫颈癌筛查的女性人群在性行为方面存在异质性,因此感染 HPV 和随后发生癌前病变的风险不同,我们使用了一个移动者-停留者混合模型,该模型假设一部分人群将保持健康状态,并且不受疾病进展的影响。由于每个状态都是表征子宫颈的三个不同测试的组合,当至少观察到一个但不是每个测试时,就会出现部分观察到的数据。标准的前向后向算法,用于评估 EM 算法中的 E-step,用于 HMM 的最大似然估计,不能将时间点与部分观察到的数据结合起来。我们提出了一种新的前向-后向算法,该算法考虑了参与者后续访问中可能发生的所有可能的完全观察状态。我们将我们的方法应用于针对轻度宫颈异常女性的大型管理试验的数据。我们的模拟研究发现,我们的方法具有相对较小的偏差,并且优于导致较大偏差的更简单方法。不能将时间点与部分观察到的数据结合起来。我们提出了一种新的前向-后向算法,该算法考虑了参与者后续访问中可能发生的所有可能的完全观察状态。我们将我们的方法应用于针对轻度宫颈异常女性的大型管理试验的数据。我们的模拟研究发现,我们的方法具有相对较小的偏差,并且优于导致较大偏差的更简单方法。不能将时间点与部分观察到的数据结合起来。我们提出了一种新的前向-后向算法,该算法考虑了参与者后续访问中可能发生的所有可能的完全观察状态。我们将我们的方法应用于针对轻度宫颈异常女性的大型管理试验的数据。我们的模拟研究发现,我们的方法具有相对较小的偏差,并且优于导致较大偏差的更简单方法。
更新日期:2021-06-05
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