Joint Meta-Regression Methods Accounting for Postrandomization Variables
考虑随机化后变量的联合元回归方法
基本信息
- 批准号:9431714
- 负责人:
- 金额:$ 21.14万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-09-05 至 2019-08-31
- 项目状态:已结题
- 来源:
- 关键词:AccountingAddressAlternative TherapiesAttentionAttenuatedBayesian ModelingBenefits and RisksBiochemical MarkersCardiovascular systemClinicalCommunitiesComputer softwareConsensusDataData AnalysesDevelopmentDiseaseDoctor of MedicineDoctor of PhilosophyDropoutDropsEvidence Based MedicineGoalsHealthcareIndividualInvestigationJointsMalignant NeoplasmsManuscriptsMeasuresMeta-AnalysisMethodsModelingOutcome MeasurePatientsPatternPhasePrincipal InvestigatorPropertyPublic HealthPublishingRandomizedRandomized Clinical TrialsReproducibilityResearch PersonnelScientistSelection for TreatmentsSourceStatistical MethodsWithdrawalarmcomparative effectivenesseffectiveness researchevidence baseexperiencefollow-upimprovedinnovationinterestnon-complianceopen sourcepillprematureprimary outcomerapid growthsimulationsoftware developmentsystematic reviewtheoriestreatment effecttreatment planningtreatment responseuser friendly software
项目摘要
Joint Meta-Regression Methods Accounting for Postrandomization Variables
Principal Investigator: Haitao Chu, M.D., Ph.D.
Summary
The rapid growth of interest in comparative effectiveness research and evidence-based medicine has led to
dramatically increased attention to systematic reviews and meta-analyses, which synthesize and contrast multi-
ple randomized clinical trials. T
o examine the impact of covariates on study-specific treatment effects, meta-
regression methods are available for conventional meta-analysis comparing two treatments and for network
meta-analysis simultaneously comparing multiple treatments
.
While there is broad consensus on methods for
examining study-level covariates which are similar across a study's treatment arms because of randomization
it is much more challenging to adjust for postrandomization variables, which are expected to differ between
treatment arms within a study. Examples include differential noncompliance, measured as the proportion of
premature treatment discontinuation or drop out, loss to follow-up, or change to an alternative therapy. To the
best of our knowledge, existing meta-regression methods only focus on
the impact of study-level covariates,
which are assumed to be fixed, while postrandomization variables are generally considered random. Thus, ex-
isting meta-regression methods cannot account for postrandomization variables.
Because postrandomization variables such as differential noncompliance can induce bias in estimating the
effect of treatment plans, in responding to PA-16-161 this proposal's overall goal is to develop cutting-edge joint
models to account for postrandomization variables in meta-analysis, and to integrate them into publicly available,
easy-to-use software to enhance the reproducibility, validity, and generalizability of meta-analyses. Specifically,
we will apply Bayesian hierarchical models in these three specific aims: 1) develop joint meta-regression meth-
ods to adjust for postrandomization variables in conventional meta-analysis; 2) develop multivariate joint meta-
regression methods to adjust for postrandomization variables in network meta-analysis; and 3) objectively eval-
uate the proposed methods and develop an open-source R package.
We will evaluate the strengths and weaknesses of these methods compared to existing meta-analysis meth-
ods, through real data applications and extensive simulations. The proposed statistical methods will be broadly
applicable to many meta-analyses. Completing these aims will substantially advance comparative effectiveness
research and evidence-based medicine through innovative meta-analysis methods. It will improve public health
by facilitating treatment selection for various cancers and for cardiovascular, infectious, and other diseases.
考虑后随机化变量的联合元回归方法
主要研究者:Haitao Chu,M.D.,博士
总结
对比较有效性研究和循证医学的兴趣迅速增长,
极大地增加了对系统综述和荟萃分析的关注,这些综述和荟萃分析综合和对比了多个
多项随机临床试验。不
检查协变量对研究特定治疗效果的影响,Meta分析
回归方法可用于比较两种治疗的常规荟萃分析和网络分析。
荟萃分析同时比较多种治疗
.
虽然在方法上存在广泛共识,
检查研究水平的协变量,由于随机化,这些协变量在研究的治疗组中相似
然而,调整随机化后的变量更具挑战性,这些变量预计在
研究中的治疗组。例子包括不同的不遵守,衡量的比例,
提前停止治疗或脱落、失访或改用替代治疗。到
据我们所知,现有的元回归方法只关注
研究水平协变量的影响,
假设其是固定的,而随机化后的变量通常被认为是随机的。因此,前-
多元回归方法不能解释随机化后的变量。
由于随机化后变量(如差异性不依从性)可能会导致估计
治疗计划的效果,在回应PA-16-161这一建议的总体目标是发展尖端联合
模型来解释荟萃分析中的随机化后变量,并将其整合到公开可用的,
易于使用的软件,以提高荟萃分析的再现性,有效性和普遍性。具体地说,
我们将在这三个具体目标中应用贝叶斯层次模型:1)开发联合元回归方法,
在传统荟萃分析中调整随机化后变量; 2)开发多变量联合Meta,
回归方法,以调整网络荟萃分析中的随机化后变量;和3)客观地评估,
评估所提出的方法,并开发一个开源的R包。
我们将评估这些方法与现有荟萃分析方法相比的优势和劣势。
ods,通过真实的数据应用和广泛的模拟。拟议的统计方法将广泛
适用于许多荟萃分析。完成这些目标将大大提高相对有效性
研究和循证医学通过创新的荟萃分析方法。它将改善公众健康
通过促进各种癌症和心血管、传染病和其他疾病的治疗选择。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Haitao Chu其他文献
Haitao Chu的其他文献
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{{ truncateString('Haitao Chu', 18)}}的其他基金
Statistical Methods and Software for Multivariate Meta-analysis
多元荟萃分析的统计方法和软件
- 批准号:
10015333 - 财政年份:2019
- 资助金额:
$ 21.14万 - 项目类别:
Statistical Methods and Software for Multivariate Meta-analysis
多元荟萃分析的统计方法和软件
- 批准号:
9815902 - 财政年份:2019
- 资助金额:
$ 21.14万 - 项目类别:
Aiding Effective Decision Making in Dental Research Using Network Meta-analysis
使用网络元分析帮助牙科研究中的有效决策
- 批准号:
8806160 - 财政年份:2015
- 资助金额:
$ 21.14万 - 项目类别:
Statistical Methods and Software for Multivariate Meta-analysis
多元荟萃分析的统计方法和软件
- 批准号:
9108437 - 财政年份:2015
- 资助金额:
$ 21.14万 - 项目类别:
Bayesian Methods and Software for Patient-Centered Network Meta-Analysis of Binar
用于以患者为中心的二进制网络荟萃分析的贝叶斯方法和软件
- 批准号:
8580883 - 财政年份:2013
- 资助金额:
$ 21.14万 - 项目类别:
Bayesian Methods and Software for Patient-Centered Network Meta-Analysis of Binar
用于以患者为中心的二进制网络荟萃分析的贝叶斯方法和软件
- 批准号:
8661112 - 财政年份:2013
- 资助金额:
$ 21.14万 - 项目类别:
Statistical Methods and Software for Meta-analysis of Diagnostic Tests
诊断测试荟萃分析的统计方法和软件
- 批准号:
8267547 - 财政年份:2011
- 资助金额:
$ 21.14万 - 项目类别:
Statistical Methods and Software for Meta-analysis of Diagnostic Tests
诊断测试荟萃分析的统计方法和软件
- 批准号:
8164771 - 财政年份:2011
- 资助金额:
$ 21.14万 - 项目类别:
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