Bayesian Methods for Latent Class Models

我的研究项目主要集中在潜在类模型估计的贝叶斯方法的发展。这些模型的动机是涉及疾病或条件的诊断测试，没有完美的测试存在的问题。这是许多常见疾病的情况，例如肺炎，儿童肺结核，前列腺癌。缺乏一个完美的测试提出了一个方法上的挑战，有兴趣在测量疾病的患病率或在评估疾病的诊断测试的研究人员。目前的建议描述了将在三个不同主题下开发的此类研究的新方法：*i）在单一研究背景下进行潜在类别分析的方法：* 在这个主题下，我们将研究比较竞争潜在类模型的方法，估算新试验增量值的方法和确定实际使用的最佳试验顺序的方法。* ii）评估诊断测试所需样本量的方法：* 在此主题下，我们将描述设计新研究的方法，其中新测试相对于旧测试的增量值是感兴趣的统计量。我们还将开发设计研究的方法，其中感兴趣的结果是可靠性而不是有效性。iii）在潜在类别荟萃分析模型中建模条件依赖和测量异质性的方法：* 在此主题下，我们将描述在荟萃分析背景下建模条件依赖的方法和在荟萃分析中报告研究间异质性的方法。一个贝叶斯推理框架将在整个过程中使用。这种方法的一个特别的优点是，它可以用于估计的不可识别的模型，经常出现时，观察到的，不完美的测试是小的（小于三个或四个）。在这种情况下，需要用关于未知参数子集的先验信息（例如，已建立但不完善的诊断测试的灵敏度和特异性）来增强观察到的数据。此外，贝叶斯方法在概念上很简单，可以应用于上述主题下出现的复杂模型。** 贝叶斯方法的实施将涉及使用蒙特卡罗马尔可夫链方法。传统上，传播贝叶斯方法的一个重要障碍是缺乏软件。因此，将开发方便用户的软件，以配合每一种拟议的方法。每个主题将产生几个适合研究生或博士后研究员的子项目。这里描述的方法也将在出现错误测量数据的其他领域中找到应用。

My research program is focused on the development of Bayesian methods for estimation of latent class models. These models are motivated by problems involving diagnostic tests for diseases or conditions for which no perfect test exists. This is the case for many common diseases e.g. pneumonia, tuberculosis in children, prostate cancer. The lack of a perfect test presents a methodological challenge to researchers interested in measuring the prevalence of the disease or in the evaluating diagnostic tests for the disease. The current proposal describes new methods for such studies that will be developed under three different themes: ******i) Methods for latent class analysis in the context of a single study: ***Under this theme, we will study methods for comparing competing latent class models, methods for estimating the incremental value of a new test and methods for determining the optimal sequence of tests to be used in practice.******ii) Methods for evaluating the sample size required for evaluating diagnostic tests: ***Under this theme, we will describe methods for designing a new study where the incremental value of a new test over an older test is the statistic of interest. We will also develop methods for designing studies where the outcome of interest is reliability rather than validity.******iii) Methods for modeling conditional dependence and measuring heterogeneity in latent class meta-analysis models: ***Under this theme, we will describe methods for modeling conditional dependence in the context of a meta-analysis and methods for reporting the between-study heterogeneity in a meta-analysis.******A Bayesian inferential framework will be used throughout. One particular advantage of this approach for the proposed research is that it can be used for estimation of the non-identifiable models that frequently arise when the number of observed, imperfect tests is small (less than three or four). In such cases, the observed data needs to be augmented with prior information on a subset of the unknown parameters (e.g. the sensitivity and specificity of a well established but imperfect diagnostic test). Further, the Bayesian approach is conceptually straightforward to apply to the complex models that will arise under the above themes. ******Implementation of the Bayesian methods will involve use of Monte Carlo Markov Chain methods. Traditionally, an important impediment in disseminating Bayesian methods has been the lack of software. Therefore, user-friendly software will be developed to accompany each of the proposed methods. Each theme will give rise to several sub-projects suitable for graduate students or post-doctoral fellows. The methods described here will also find application in other areas where mis-measured data arise.