Statistical Methods for Longitudinal Data and Meta-Analysis

纵向数据的统计方法和荟萃分析

• 批准号: MC_EX_G0902100
• 负责人: Jessica Barrett
• 金额: $ 17.15万
• 依托单位: MRC Biostatistics Unit
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MC_EX_G0902100
• 关键词: Statistical; Methods; Longitudinal; Data; Meta

One aspect of my work is to develop statistical methods to analyse data from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Data from this trial consists of up to six annual sets of questionnaires from each participant. The questionnaires assess anxiety, sexual function and acceptability of the screening process. I will model how these three processes change over time, and how they depend on one another. There are several approaches to analysing this type of data, and I will explore the pros and cons of three different methods. The challenge in this work lies in the complexity of the UKCTOCS data-set. I will also use some of these methods to analyse data from the MRC Cognitive Function and Ageing Study.A second aspect of my work is to develop methods for use in meta-analysis. Meta-analysis refers to the pooling of data from different studies, and is used to provide an overview of the available evidence. I will investigate methods for the meta-analysis of data which consists of times to events. I will also explore the meta-analysis of treatment `networks', investigating the relative effects of a set of treatments for a given condition.

我工作的一个方面是开发统计方法来分析英国卵巢癌筛查合作试验(UKCTOCS)的数据。这项试验的数据由每位参与者每年最多六份问卷组成。这些问卷评估了焦虑、性功能和筛查过程的可接受性。我将对这三个过程如何随时间变化以及它们如何相互依赖进行建模。有几种方法可以分析这种类型的数据，我将探索三种不同方法的优缺点。这项工作的挑战在于UKCTOCS数据集的复杂性。我还将使用这些方法中的一些来分析来自MRC认知功能和老龄化研究的数据。我工作的第二个方面是开发用于Meta分析的方法。Meta分析是指汇集来自不同研究的数据，并用于提供现有证据的概述。我将研究对数据进行元分析的方法，它由时间到事件组成。我还将探讨治疗“网络”的荟萃分析，调查一组治疗在特定情况下的相对效果。

项目成果

Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference.

• DOI: 10.1111/rssb.12060
• 发表时间: 2015-01
• 期刊: Journal of the Royal Statistical Society. Series B, Statistical methodology
• 作者: Barrett J;Diggle P;Henderson R;Taylor-Robinson D
• 通讯作者: Taylor-Robinson D

Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies.

• DOI: 10.1002/jrsm.1044
• 发表时间: 2012-06
• 期刊: RESEARCH SYNTHESIS METHODS
• 影响因子: 9.8
• 作者: Higgins, J. P. T.;Jackson, D.;Barrett, J. K.;Lu, G.;Ades, A. E.;White, I. R.
• 通讯作者: White, I. R.

Doubly Robust Estimation of Optimal Dynamic Treatment Regimes.

• DOI: 10.1007/s12561-013-9097-6
• 发表时间: 2014
• 期刊: STATISTICS IN BIOSCIENCES
• 影响因子: 1
• 作者: Barrett, Jessica K;Henderson, Robin;Rosthoj, Susanne
• 通讯作者: Rosthoj, Susanne

Dynamic predictions using flexible joint models of longitudinal and time-to-event data.

使用纵向和事件时间数据的灵活联合模型进行动态预测。

• DOI: 10.1002/sim.7209
• 发表时间: 2017-04-30
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Barrett J;Su L
• 通讯作者: Su L

Two-stage meta-analysis of survival data from individual participants using percentile ratios.

• DOI: 10.1002/sim.5516
• 发表时间: 2012-12-30
• 期刊: STATISTICS IN MEDICINE
• 影响因子: 2
• 作者: Barrett, Jessica K.;Farewell, Vern T.;Siannis, Fotios;Tierney, Jayne;Higgins, Julian P. T.
• 通讯作者: Higgins, Julian P. T.