The SMART Safety: An empirical dataset for evidence synthesis of adverse events

Fan, Shiqi; Yu, Tianqi; Yang, Xi; Zhang, Rui; Furuya-Kanamori, Luis; Xu, Chang

doi:10.1016/j.dib.2023.109639

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…The study is based on our recently constructed large empirical dataset, known as SMART Safety [ 23 , 24 ]. The foundations of this dataset stem from a PubMed literature search conducted on July 28, 2020, by an information specialist, with the aim of retrieving systematic reviews of medication harms that were published (including online first) between January 1, 2015, and January 1, 2020.…”

Section: Methodsmentioning

confidence: 99%

Influence of lack of blinding on the estimation of medication-related harms: a retrospective cohort study of randomized controlled trials

Xu,

Zhang,

Doi

et al. 2024

BMC Med

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Background Empirical evidence suggests that lack of blinding may be associated with biased estimates of treatment benefit in randomized controlled trials, but the influence on medication-related harms is not well-recognized. We aimed to investigate the association between blinding and clinical trial estimates of medication-related harms. Methods We searched PubMed from January 1, 2015, till January 1, 2020, for systematic reviews with meta-analyses of medication-related harms. Eligible meta-analyses must have contained trials both with and without blinding. Potential covariates that may confound effect estimates were addressed by restricting trials within the comparison or by hierarchical analysis of harmonized groups of meta-analyses (therefore harmonizing drug type, control, dosage, and registration status) across eligible meta-analyses. The weighted hierarchical linear regression was then used to estimate the differences in harm estimates (odds ratio, OR) between trials that lacked blinding and those that were blinded. The results were reported as the ratio of OR (ROR) with its 95% confidence interval (CI). Results We identified 629 meta-analyses of harms with 10,069 trials. We estimated a weighted average ROR of 0.68 (95% CI: 0.53 to 0.88, P < 0.01) among 82 trials in 20 meta-analyses where blinding of participants was lacking. With regard to lack of blinding of healthcare providers or outcomes assessors, the RORs were 0.68 (95% CI: 0.53 to 0.87, P < 0.01 from 81 trials in 22 meta-analyses) and 1.00 (95% CI: 0.94 to 1.07, P = 0.94 from 858 trials among 155 meta-analyses) respectively. Sensitivity analyses indicate that these findings are applicable to both objective and subjective outcomes. Conclusions Lack of blinding of participants and health care providers in randomized controlled trials may underestimate medication-related harms. Adequate blinding in randomized trials, when feasible, may help safeguard against potential bias in estimating the effects of harms.

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Section: Methodsmentioning

confidence: 99%

Influence of lack of blinding on the estimation of medication-related harms: a retrospective cohort study of randomized controlled trials

Xu,

Zhang,

Doi

et al. 2024

BMC Med

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Towards the automatic risk of bias assessment on randomized controlled trials: A comparison of RobotReviewer and humans

Tian,

Yang,

Doi

et al. 2024

Research Synthesis Methods

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RobotReviewer is a tool for automatically assessing the risk of bias in randomized controlled trials, but there is limited evidence of its reliability. We evaluated the agreement between RobotReviewer and humans regarding the risk of bias assessment based on 1955 randomized controlled trials. The risk of bias in these trials was assessed via two different approaches: (1) manually by human reviewers, and (2) automatically by the RobotReviewer. The manual assessment was based on two groups independently, with two additional rounds of verification. The agreement between RobotReviewer and humans was measured via the concordance rate and Cohen's kappa statistics, based on the comparison of binary classification of the risk of bias (low vs. high/unclear) as restricted by RobotReviewer. The concordance rates varied by domain, ranging from 63.07% to 83.32%. Cohen's kappa statistics showed a poor agreement between humans and RobotReviewer for allocation concealment (κ = 0.25, 95% CI: 0.21–0.30), blinding of outcome assessors (κ = 0.27, 95% CI: 0.23–0.31); While moderate for random sequence generation (κ = 0.46, 95% CI: 0.41–0.50) and blinding of participants and personnel (κ = 0.59, 95% CI: 0.55–0.64). The findings demonstrate that there were domain‐specific differences in the level of agreement between RobotReviewer and humans. We suggest that it might be a useful auxiliary tool, but the specific manner of its integration as a complementary tool requires further discussion.

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Harm effects in non-registered versus registered randomized controlled trials of medications: a retrospective cohort study of clinical trials

Xu,

Fan,

Furuya-Kanamori

et al. 2024

BMC Med

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The SMART Safety: An empirical dataset for evidence synthesis of adverse events

Cited by 4 publications

References 9 publications

Influence of lack of blinding on the estimation of medication-related harms: a retrospective cohort study of randomized controlled trials

Influence of lack of blinding on the estimation of medication-related harms: a retrospective cohort study of randomized controlled trials

Towards the automatic risk of bias assessment on randomized controlled trials: A comparison of RobotReviewer and humans

Harm effects in non-registered versus registered randomized controlled trials of medications: a retrospective cohort study of clinical trials

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