Joint Meta-Regression Methods Accounting for Postrandomization Variables
Joint Meta-Regression Methods Accounting for Postrandomization Variables
批准号:
9431714
负责人:
Haitao Chu
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
项目状态:
已结题
起止时间:
2017-09-05 至 2019-08-31
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
中文摘要
考虑后随机变量的联合元回归方法
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Statistical Methods and Software for Multivariate Meta-analysis
-
批准号:10015333
-
项目类别:
-
资助金额:$32.55万
-
财政年份:2019
-
负责人:Haitao Chu
-
依托单位:
Statistical Methods and Software for Multivariate Meta-analysis
-
批准号:9815902
-
项目类别:
-
资助金额:$33.92万
-
财政年份:2019
-
负责人:Haitao Chu
-
依托单位:
Aiding Effective Decision Making in Dental Research Using Network Meta-analysis
-
批准号:8806160
-
项目类别:
-
资助金额:$14.62万
-
财政年份:2015
-
负责人:Haitao Chu
-
依托单位:
Statistical Methods and Software for Multivariate Meta-analysis
-
批准号:9108437
-
项目类别:
-
资助金额:$20.38万
-
财政年份:2015
-
负责人:Haitao Chu
-
依托单位:
Bayesian Methods and Software for Patient-Centered Network Meta-Analysis of Binar
-
批准号:8580883
-
项目类别:
-
资助金额:$16.81万
-
财政年份:2013
-
负责人:Haitao Chu
-
依托单位:
Bayesian Methods and Software for Patient-Centered Network Meta-Analysis of Binar
-
批准号:8661112
-
项目类别:
-
资助金额:$21.65万
-
财政年份:2013
-
负责人:Haitao Chu
-
依托单位:
Statistical Methods and Software for Meta-analysis of Diagnostic Tests
-
批准号:8267547
-
项目类别:
-
资助金额:$4.99万
-
财政年份:2011
-
负责人:Haitao Chu
-
依托单位:
Statistical Methods and Software for Meta-analysis of Diagnostic Tests
-
批准号:8164771
-
项目类别:
-
资助金额:$4.99万
-
财政年份:2011
-
负责人:Haitao Chu
-
依托单位:
海外基金