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  • Furuya-Kanamori, Luis  (5)
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  • 1
    In: BMC Medicine, Springer Science and Business Media LLC, Vol. 19, No. 1 ( 2021-12)
    Abstract: Zero-events studies frequently occur in systematic reviews of adverse events, which consist of an important source of evidence. We aimed to examine how evidence of zero-events studies was utilized in the meta-analyses of systematic reviews of adverse events. Methods We conducted a survey of systematic reviews published in two periods: January 1, 2015, to January 1, 2020, and January 1, 2008, to April 25, 2011. Databases were searched for systematic reviews that conducted at least one meta-analysis of any healthcare intervention and used adverse events as the exclusive outcome. An adverse event was defined as any untoward medical occurrence in a patient or subject in healthcare practice. We summarized the frequency of occurrence of zero-events studies in eligible systematic reviews and how these studies were dealt with in the meta-analyses of these systematic reviews. Results We included 640 eligible systematic reviews. There were 406 (63.45%) systematic reviews involving zero-events studies in their meta-analyses, among which 389 (95.11%) involved single-arm-zero-events studies and 223 (54.93%) involved double-arm-zero-events studies. The majority (98.71%) of these systematic reviews incorporated single-arm-zero-events studies into the meta-analyses. On the other hand, the majority (76.23%) of them excluded double-arm-zero-events studies from the meta-analyses, of which the majority (87.06%) did not discuss the potential impact of excluding such studies. Systematic reviews published at present (2015-2020) tended to incorporate zero-events studies in meta-analyses than those published in the past (2008-2011), but the difference was not significant (proportion difference=−0.09, 95% CI −0.21 to 0.03, p = 0.12). Conclusion Systematic review authors routinely treated studies with zero-events in both arms as “non-informative” carriers and excluded them from their reviews. Whether studies with no events are “informative” or not largely depends on the methods and assumptions applied, thus sensitivity analyses using different methods should be considered in future meta-analyses.
    Type of Medium: Online Resource
    ISSN: 1741-7015
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2131669-7
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  • 2
    In: BMJ, BMJ
    Abstract: To investigate the validity of data extraction in systematic reviews of adverse events, the effect of data extraction errors on the results, and to develop a classification framework for data extraction errors to support further methodological research. Design Reproducibility study. Data sources PubMed was searched for eligible systematic reviews published between 1 January 2015 and 1 January 2020. Metadata from the randomised controlled trials were extracted from the systematic reviews by four authors. The original data sources (eg, full text and ClinicalTrials.gov) were then referred to by the same authors to reproduce the data used in these meta-analyses. Eligibility criteria for selecting studies Systematic reviews were included when based on randomised controlled trials for healthcare interventions that reported safety as the exclusive outcome, with at least one pair meta-analysis that included five or more randomised controlled trials and with a 2×2 table of data for event counts and sample sizes in intervention and control arms available for each trial in the meta-analysis. Main outcome measures The primary outcome was data extraction errors summarised at three levels: study level, meta-analysis level, and systematic review level. The potential effect of such errors on the results was further investigated. Results 201 systematic reviews and 829 pairwise meta-analyses involving 10 386 randomised controlled trials were included. Data extraction could not be reproduced in 1762 (17.0%) of 10 386 trials. In 554 (66.8%) of 829 meta-analyses, at least one randomised controlled trial had data extraction errors; 171 (85.1%) of 201 systematic reviews had at least one meta-analysis with data extraction errors. The most common types of data extraction errors were numerical errors (49.2%, 867/1762) and ambiguous errors (29.9%, 526/1762), mainly caused by ambiguous definitions of the outcomes. These categories were followed by three others: zero assumption errors, misidentification, and mismatching errors. The impact of these errors were analysed on 288 meta-analyses. Data extraction errors led to 10 (3.5%) of 288 meta-analyses changing the direction of the effect and 19 (6.6%) of 288 meta-analyses changing the significance of the P value. Meta-analyses that had two or more different types of errors were more susceptible to these changes than those with only one type of error (for moderate changes, 11 (28.2%) of 39 v 26 (10.4%) 249, P=0.002; for large changes, 5 (12.8%) of 39 v 8 (3.2%) of 249, P=0.01). Conclusion Systematic reviews of adverse events potentially have serious issues in terms of the reproducibility of the data extraction, and these errors can mislead the conclusions. Implementation guidelines are urgently required to help authors of future systematic reviews improve the validity of data extraction.
    Type of Medium: Online Resource
    ISSN: 1756-1833
    Language: English
    Publisher: BMJ
    Publication Date: 2022
    detail.hit.zdb_id: 1479799-9
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  • 3
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  BMC Medical Research Methodology Vol. 23, No. 1 ( 2023-03-13)
    In: BMC Medical Research Methodology, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2023-03-13)
    Abstract: In evidence synthesis practice, dealing with studies with no cases in both arms has been a tough problem, for which there is no consensus in the research community. In this study, we propose a method to measure the potential impact of studies with no cases for meta-analysis results which we define as harms index (Hi) and benefits index (Bi) as an alternative solution for deciding how to deal with such studies. Methods Hi and Bi are defined by the minimal number of cases added to the treatment arm (Hi) or control arm (Bi) of studies with no cases in a meta-analysis that lead to a change of the direction of the estimates or its statistical significance. Both exact and approximating methods are available to calculate Hi and Bi. We developed the “hibi” module in Stata so that researchers can easily implement the method. A real-world investigation of meta-analyses from Cochrane reviews was employed to evaluate the proposed method. Results Based on Hi and Bi, our results suggested that 21.53% (Hi) to 26.55% (Bi) of Cochrane meta-analyses may be potentially impacted by studies with no cases, for which studies with no cases could not be excluded from the synthesis. The approximating method shows excellent specificity (100%) for both Hi and Bi, moderate sensitivity (68.25%) for Bi, and high sensitivity (80.61%) for Hi compared to the exact method. Conclusions The proposed method is practical and useful for systematic reviewers to measure whether studies with no cases impact the results of meta-analyses and may act as an alternative solution for review authors to decide whether to include studies with no events for the synthesis or not.
    Type of Medium: Online Resource
    ISSN: 1471-2288
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2041362-2
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  • 4
    Online Resource
    Online Resource
    Elsevier BV ; 2021
    In:  Journal of Clinical Epidemiology Vol. 135 ( 2021-07), p. 70-78
    In: Journal of Clinical Epidemiology, Elsevier BV, Vol. 135 ( 2021-07), p. 70-78
    Type of Medium: Online Resource
    ISSN: 0895-4356
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 1500490-9
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  • 5
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
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