Unbiased estimation in hierarchical models for Clinical trials

临床试验分层模型中的无偏估计

• 批准号: 2884930
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2884930
• 关键词: Unbiased; estimation; hierarchical; models; Clinical

Background to the projectIn this project, we aim to develop unbiased estimation for hierarchical data, organised in multiple levels or a tree pattern structure. Such data can arise from:cluster trials or multisite randomised controlled trialssubgroup analysismeta-analysis of trialsWe assume there is a common effect across all clusters (or sites/subgroups/trials in a meta-analysis) and each cluster (or etc) has its own cluster-specific effect.Research shows that the estimates of the common and cluster-specific effects can be biased. The bias will overestimate the common effect and lead to misleading false significant conclusions. The problem is more severe for large number of clusters with relatively small cluster sizes. In the extreme case when the size of each cluster is 2, the relative bias for the estimate of the common effect can be as large as 100% for binary outcomes , .What the studentship will encompassOne plausible solution is to regard the cluster-specific effect as a random variable from a latent distribution. However, the choice of distribution is often difficult to verify, and the resulting inferences for the common effect may be sensitive to the choice of the distribution .Alternatively, one may use conditional/composite likelihoods to obtain unbiased estimate of the common effect by eliminating cluster-specific effects in estimation. The unbiased estimate of common effect then can be used to estimate unbiased cluster-specific effects using a profile likelihood method.Motivated by data from a series of real clinical trials, this project will involve1) Understanding and illustrating the bias in hierarchical data2) Developing pseudo-likelihood methods for unbiased estimation for hierarchical data with different types of outcomes3) Investigating the power, sample size needed and the impact of intra-cluster correlation on the unbiased estimators4) Developing Expectation-maximization algorithm for random common and cluster-specific effects 5) Exploring to extend existing methods in meta-analysis to handle heterogeneity in hierarchical trial data.

在这个项目中，我们的目标是为分层数据开发无偏估计，组织在多个层次或树形结构中。这些数据可以来自：聚类试验或多地点随机对照试验、亚组分析、试验的荟萃分析。我们假设所有聚类（或荟萃分析中的地点/亚组/试验）都有一个共同的效应，每个聚类（或等）都有自己的聚类特定效应。研究表明，对共同效应和特定效应的估计可能存在偏差。偏差会高估共同效应，并导致误导性的虚假显著结论。对于相对较小的集群规模的大量集群，这个问题更为严重。在极端情况下，当每个簇的大小为2时，对于二元结果，估计共同效应的相对偏差可能高达100%。一个合理的解决方案是将集群特定效应视为潜在分布中的随机变量。然而，分布的选择往往难以验证，并且对共同效应的推断可能对分布的选择很敏感。或者，可以使用条件/复合似然，通过消除估计中的簇特定效应来获得对共同效应的无偏估计。然后，共同效应的无偏估计可用于使用轮廓似然方法估计无偏簇特定效应。在一系列真实临床试验数据的激励下，本项目将包括：1)理解和说明分层数据中的偏倚；2)开发伪似然方法，对具有不同结果类型的分层数据进行无偏估计；需要的样本量和簇内相关性对无偏估计量的影响4)开发随机共同效应和簇特异性效应的期望最大化算法5)探索扩展现有的元分析方法来处理分层试验数据的异质性。