Clinical trials : journal of the Society for Clinical Trials
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Randomized Controlled Trial
Parent perspectives on consent for the linkage of data to evaluate vaccine safety: a randomised trial of opt-in and opt-out consent.
We examined parents' consent preferences and understanding of an opt-in or opt-out invitation to participate in data linkage for post-marketing safety surveillance of childhood vaccines. ⋯ This trial demonstrates that informed consent for a population-based surveillance programme cannot realistically be achieved using mail-based opt-in and opt-out approaches. While recall and understanding of the study's purpose were better among parents who actively consented (opted in) compared with parents who passively consented (did not opt out), participation was substantially lower (21% vs. 96% respectively). Most parents appeared to have a poor understanding of data linkage for vaccine safety surveillance; nonetheless, they supported data linkage. They preferred a system utilising opt-out consent or no consent to one using opt-in consent.
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There are many benefits of data sharing, including the promotion of new research from effective use of existing data, replication of findings through re-analysis of pooled data files, meta-analysis using individual patient data, and reinforcement of open scientific inquiry. A randomized controlled trial is considered as the 'gold standard' for establishing treatment effectiveness, but clinical trial research is very costly, and sharing data is an opportunity to expand the investment of the clinical trial beyond its original goals at minimal costs. ⋯ The Data Share website offers researchers easy access to de-identified data files with the goal to promote additional research and identify new findings from completed CTN studies. To maximize the utility of the website, ongoing collaborative efforts are needed to standardize the core measures used for data collection in the CTN studies with the goal to increase their comparability and to facilitate the ability to pool data files for cross-study analyses.
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Due to the sparse nature of serious drug-related adverse events (AEs), meta-analyses combining data from several randomized controlled trials (RCTs) to evaluate drug safety issues are increasingly being conducted and published, influencing clinical and regulatory decision making. Evaluation of meta-analyses involves the assessment of both the individual constituent trials and the approaches used to combine them. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting framework is designed to enhance the reporting of systematic reviews and meta-analyses. However, PRISMA may not cover all critical elements useful in the evaluation of meta-analyses with a focus on drug safety particularly in the regulatory-public health setting. ⋯ While the majority of PRISMA elements were addressed by most studies reviewed, the majority of studies did not address most of the additional safety-related elements. These findings highlight the need for the development and validation of a drug safety reporting framework and the importance of the current initiative by the Council for International Organizations of Medical Sciences (CIOMS) to create a guidance document for drug safety information synthesis/meta-analysis, which may improve reporting, conduct, and evaluation of meta-analyses of drug safety and inform clinical and regulatory decision making.
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Comparative Study
Evaluating the impact of imputations for missing participant outcome data in a network meta-analysis.
In a meta-analysis of trials with missing outcome data, a parameter known as informative missing odds ratio (IMOR) can be used to quantify the relationship between informative missingness and a binary outcome. IMORs also account for the increased uncertainty due to missingness in the meta-analysis results. ⋯ Sensitivity analysis to account for missing outcome data and their uncertainty in the NMA can be undertaken by extending the idea of IMOR. In two case examples, we found no differences between the various models due to low missing data rate. In line with previous observations, data carry little information about the reason of missingness.