Research Synthesis Methods · 2024 · 25 citations · 26 references
Public HealthBiostatisticsEvidence-based PracticeMeta-analysisResearch EthicsClinical TrialsData IntegrityData Integrity AssessmentData ReviewIndividual Participant Data Meta-analysisRandomized Controlled TrialsClinical Trial Data IntegrityClinical Trial IntegrityDecision RulesPilot Randomized Controlled TrialsPilot TrialsRandomized Controlled Trial AppraisalRandomized Controlled Trial EthicsSystematic Literature Review
Increasing concerns about the trustworthiness of research have prompted calls to scrutinise studies' Individual Participant Data (IPD), but guidance on how to do this was lacking. To address this, we developed the IPD Integrity Tool to screen randomised controlled trials (RCTs) for integrity issues. Development of the tool involved a literature review, consultation with an expert advisory group, piloting on two IPD meta-analyses (including 73 trials with IPD), preliminary validation on 13 datasets with and without known integrity issues, and evaluation to inform iterative refinements. The IPD Integrity Tool comprises 31 items (13 study-level, 18 IPD-specific). IPD-specific items are automated where possible, and are grouped into eight domains, including unusual data patterns, baseline characteristics, correlations, date violations, patterns of allocation, internal and external inconsistencies, and plausibility of data. Users rate each item as having either no issues, some/minor issue(s), or many/major issue(s) according to decision rules, and justification for each rating is recorded. Overall, the tool guides decision-making by determining whether a trial has no concerns, some concerns requiring further information, or major concerns warranting exclusion from evidence synthesis or publication. In our preliminary validation checks, the tool accurately identified all five studies with known integrity issues. The IPD Integrity Tool enables users to assess the integrity of RCTs via examination of IPD. The tool may be applied by evidence synthesists, editors and others to determine whether an RCT should be considered sufficiently trustworthy to contribute to the evidence base that informs policy and practice.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
World Medical Association Declaration of Helsinki
JAMA · 2013 · 29.4K citations
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David Moher, Kenneth F. Schulz, Iveta Simera et al. · PLoS Medicine · 2010 · 1.5K citations · Full text