Bayesian Methods for Latent Class Models

潜在类模型的贝叶斯方法

• 批准号: RGPIN-2019-06713
• 负责人: Dendukuri, Nandini
• 金额: $ 1.17万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716024
• 关键词: Bayesian; Methods; Latent; Class; Models

Introduction: There is no perfect test for many common diseases like pneumonia. This complicates the problem of estimating disease prevalence (i.e. probability of disease in a population) or the problem of evaluating a new test in terms of sensitivity (true positive probability) and specificity (true negative probability). LCMs provide a realistic way to quantify the uncertainty in these problems. In its simplest form LCM assumes the observed variables are independent conditional on the latent disease status, i.e. errors on the tests are not correlated. Applications of these models have been slow to catch on because a number of interesting theoretical challenges remain. The long-term goal of my research program is to make Bayesian methods for Latent Class Models more robust, computationally fast and widely accessible. Objectives The current proposal proposes projects under three broad themes: 1) Latent class analysis in the context of a single study, 2) Sample size determination for latent class analysis, 3) Latent class analysis in the context of a meta-analysis. A literature review is presented separately under each theme. Methods: Under the first theme, I will explore methods for comparing competing latent class models, defining simpler models based on random effects, specification of prior distributions for LCMs as well as improved computational methods. Under the second theme, I will consider the use of approximate estimation methods to improve the speed of sample size calculations and the development of methods that consider cost and conditional dependence in sample size calculations. Under the third theme, I will extend my previous work on quantifying heterogeneity in a meta-analysis setting using the I-squared statistic. I will also cover the challenges in developing a clinical prediction model using individual-patient data analysis. Throughout a Bayesian approach will be used for estimation and inference. This approach is particularly valuable in the context of latent class models as they can be non-identifiable even when many tests are observed, and the sample size is large. Estimation of the unknown parameters is then only possible when using a Bayesian approach with informative prior distributions. Significance of research program: With continuous support from NSERC over the last 18 years I have supported numerous MSc and PhD students to develop new methods in this area, contributing to the pool of highly-qualified professionals for the Canadian workforce. Methods we have developed have been applied to support important health care research projects and policy making related to diagnostic tests.

简介： 对于肺炎等许多常见疾病，尚无完美的检测方法。这使得估计疾病流行率（即人群中患病的概率）的问题或根据敏感性（真阳性概率）和特异性（真阴性概率）评估新测试的问题变得复杂。 LCM 提供了一种量化这些问题的不确定性的现实方法。在最简单的形式中，LCM 假设观察到的变量与潜在疾病状态无关，即测试错误不相关。 这些模型的应用进展缓慢，因为仍然存在许多有趣的理论挑战。我的研究计划的长期目标是使潜在类模型的贝叶斯方法更加稳健、计算速度更快且可广泛使用。 目标 当前提案提出了三大主题下的项目：1）单个研究背景下的潜在类别分析，2）潜在类别分析的样本量确定，3）荟萃分析背景下的潜在类别分析。每个主题下都单独进行了文献综述。 方法：在第一个主题下，我将探索比较竞争潜在类模型的方法、基于随机效应定义更简单的模型、LCM 先验分布的规范以及改进的计算方法。在第二个主题下，我将考虑使用近似估计方法来提高样本量计算的速度，以及开发在样本量计算中考虑成本和条件依赖性的方法。在第三个主题下，我将扩展我之前使用 I 平方统计量在荟萃分析环境中量化异质性的工作。我还将介绍使用个体患者数据分析开发临床预测模型的挑战。 在整个过程中，贝叶斯方法将用于估计和推理。这种方法在潜在类模型的背景下特别有价值，因为即使观察到许多测试并且样本量很大，它们也可能是不可识别的。只有当使用具有信息丰富的先验分布的贝叶斯方法时，才能估计未知参数。 研究计划的意义：在过去 18 年 NSERC 的持续支持下，我支持众多硕士和博士生在该领域开发新方法，为加拿大劳动力队伍培养高素质专业人员做出了贡献。我们开发的方法已应用于支持与诊断测试相关的重要医疗保健研究项目和政策制定。