Analysis of Practices to Promote Reproducibility and Transparency in Anaesthesiology Research: Are Important Aspects “Hidden Behind the Drapes?”

Okonya, Ochije; Rorah, Drayton; Tritz, Daniel; Umberham, Blake; Wiley, Matt; Vassar, Matt

doi:10.1101/729129

Cited by 1 publication

(9 citation statements)

References 32 publications

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“…More literature searches identified another 44 eligible reports for inclusion, giving a total of 114 eligible meta-research studies examining a combined total of 2 254 031 primary articles for the review. 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 ...…”

Section: Resultsmentioning

confidence: 99%

“…Supplementary results in the supplementary information provides further information on the individual participant data retrieval process, as well as the outcomes of the data integrity checks. In total, 108 reports of 105 meta-research studies assessing a total of 2 121 580 primary articles were included in the quantitative analysis, 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 ...…”

Section: Resultsmentioning

confidence: 99%

“…Johnson 2021 65 Rauh 2022 96 Hughes 2019 58 Adewumi 2021 27 Anderson 2019 29 Smith 2021 112 Wallach 2018 121 Okonya 2019 88 Hamilton 2022 Anderson 2021 30 Fladie 2019 44 Kobres 2019 71 Random effects (GLMM method) Random effects (HKSJ method) Prediction interval Heterogeneity: I 2 =96%, τ 2 =0.02, P<0.01 0.00 (0.00 to 0.07) 0.00 (0.00 to 0.26) 0.01 (0.00 to 0.05) 0.01 (0.01 to 0.04) 0.03 (0.01 to 0.06) 0.03 (0.02 to 0.03) 0.03 (0.01 to 0.07) 0.04 (0.02 to 0.08) 0.04 (0.02 to 0.10) 0.05 (0.02 to 0.10) 0.05 (0.03 to 0.10) 0.07 (0.04 to 0.13) 0.07 (0.04 to 0.12) 0.07 (0.04 to 0. to covid-19. Also, we examined differences in pooled proportions and risk ratios when generalised linear mixed models were used to aggregate findings.…”

Section: Serghiou 2021bmentioning

confidence: 99%

“…Johnson 2021 65 Fladie Ascha Okonya 2019 88 Smith Uribe 2022 118 Walters 2019 122 Hughes 2019 58 Louderback 2023 74 Anderson 2019 29 Anderson 2021 30 Serghiou 2021a 25 Adewumi 2021 27…”

Section: Evansmentioning

confidence: 99%

“…In total, 95 and 58 meta-research studies, respectively, examined the prevalence of public data and code sharing in primary articles, with five studies examining compliance of publicly shared data with the FAIR principles. In contrast, 10, four, and two studies, respectively, assessed whether study data, code, or Evans 2019 42 Fladie 2019 45 Hardwicke 2022 53 Johnson 2020 64 Rauh 2020 98 Rauh 2022 97 Sherry 2020 110 Wallach 2018 121 Walters 2019 122 Wright 2020 126 Anderson 2019 29 Ascha 2022 31 Hughes 2019 58 Johnson 2021 65 Rauh 2022 96 So -Mahmudi 2022 113 Uribe 2022 118 Serghiou 2021a 25 Anderson 2021 30 Smith 2021 112 Serghiou 2021b 25 Fladie 2019 44 Adewumi 2021 27 Okonya 2019 88 Hamilton 2022 26 Kobres 2019 71 Random effects (GLMM method) Random effects (HKSJ method) Prediction interval Heterogeneity: I 2 =89%, τ 2 =0.01, P<0.01 0.00 (0.00 to 0.03) 0.00 (0.00 to 0.03) 0.00 (0.00 to 0.07) 0.00 (0.00 to 0.04) 0.00 (0.00 to 0.02) 0.00 (0.00 to 0.03) 0.00 (0.00 to 0.03) 0.00 (0.00 to 0.07) 0.00 (0.00 to 0.03) 0.00 (0.00 to 0.03) 0.00 (0.00 to 0.04) 0.00 (0.00 to 0.02) 0.00 (0.00 to 0.03) 0.00 (0.00 to 0.03) 0.00 (0.00 to 0.03) 0.00 (0.00 to 0.26) 0.00 (0.00 to 0.00) 0.00 (0.00 to 0.…”

Section: Fladiementioning

confidence: 99%

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Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data

Hamilton

Hong

Fraser

et al. 2023

BMJ

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Objectives To synthesise research investigating data and code sharing in medicine and health to establish an accurate representation of the prevalence of sharing, how this frequency has changed over time, and what factors influence availability. Design Systematic review with meta-analysis of individual participant data. Data sources Ovid Medline, Ovid Embase, and the preprint servers medRxiv, bioRxiv, and MetaArXiv were searched from inception to 1 July 2021. Forward citation searches were also performed on 30 August 2022. Review methods Meta-research studies that investigated data or code sharing across a sample of scientific articles presenting original medical and health research were identified. Two authors screened records, assessed the risk of bias, and extracted summary data from study reports when individual participant data could not be retrieved. Key outcomes of interest were the prevalence of statements that declared that data or code were publicly or privately available (declared availability) and the success rates of retrieving these products (actual availability). The associations between data and code availability and several factors (eg, journal policy, type of data, trial design, and human participants) were also examined. A two stage approach to meta-analysis of individual participant data was performed, with proportions and risk ratios pooled with the Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis. Results The review included 105 meta-research studies examining 2 121 580 articles across 31 specialties. Eligible studies examined a median of 195 primary articles (interquartile range 113-475), with a median publication year of 2015 (interquartile range 2012-2018). Only eight studies (8%) were classified as having a low risk of bias. Meta-analyses showed a prevalence of declared and actual public data availability of 8% (95% confidence interval 5% to 11%) and 2% (1% to 3%), respectively, between 2016 and 2021. For public code sharing, both the prevalence of declared and actual availability were estimated to be <0.5% since 2016. Meta-regressions indicated that only declared public data sharing prevalence estimates have increased over time. Compliance with mandatory data sharing policies ranged from 0% to 100% across journals and varied by type of data. In contrast, success in privately obtaining data and code from authors historically ranged between 0% and 37% and 0% and 23%, respectively. Conclusions The review found that public code sharing was persistently low across medical research. Declarations of data sharing were also low, increasing over time, but did not always correspond to actual sharing of data. The effectiveness of mandatory data sharing policies varied substantially by journal and type of data, a finding that might be informative for policy makers when designing policies and allocating resources to audit compliance. Systematic review registration Open Science Framework doi: 10.17605/OSF.IO/7SX8U .

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Serghiou 2021bmentioning

confidence: 99%

“…Johnson 2021 65 Fladie Ascha Okonya 2019 88 Smith Uribe 2022 118 Walters 2019 122 Hughes 2019 58 Louderback 2023 74 Anderson 2019 29 Anderson 2021 30 Serghiou 2021a 25 Adewumi 2021 27…”

Section: Evansmentioning

confidence: 99%