Reproducibility of COVID-19 pre-prints

Decatastrophizing research irreproducibility

The reported inability to replicate research findings from the published literature precipitated extensive efforts to identify and correct perceived deficiencies in the execution and reporting of biomedical research. Despite these efforts, quantification of the magnitude of irreproducible research or the effectiveness of associated remediation initiatives, across diverse biomedical disciplines, has made little progress over the last decade. The idea that science is self-correcting has been further challenged in recent years by the proliferation of unverified or fraudulent scientific content generated by predatory journals, paper mills, pre-print server postings, and the inappropriate use of artificial intelligence technologies. The degree to which the field of pharmacology has been negatively impacted by these evolving pressures is unknown. Regardless of these ambiguities, pharmacology societies and their associated journals have championed best practices to enhance the experimental rigor and reporting of pharmacological research. The value of transparent and independent validation of raw data generation and its analysis in basic and clinical research is exemplified by the discovery, development, and approval of Highly Effective Modulator Therapy (HEMT) for Cystic Fibrosis (CF) patients. This provides a didactic counterpoint to concerns regarding the current state of biomedical research. Key features of this important therapeutic advance include objective construction of basic and translational research hypotheses, associated experimental designs, and validation of experimental effect sizes with quantitative alignment to meaningful clinical endpoints with input from the FDA, which enhanced scientific rigor and transparency with real world deliverables for patients in need.

Reproducibility of COVID-era infectious disease models

Infectious disease modelling has been prominent throughout the COVID-19 pandemic, helping to understand the virus' transmission dynamics and inform response policies. Given their potential importance and translational impact, we evaluated the computational reproducibility of infectious disease modelling articles from the COVID era. We found that four out of 100 randomly sampled studies released between January 2020 and August 2022 could be completely computationally reproduced using the resources provided (e.g., code, data, instructions) whilst a further eight were partially reproducible. For the 100 most highly cited articles from the same period we found that 11 were completely reproducible with a further 22 partially reproducible. Reflecting on our experience, we discuss common issues affecting computational reproducibility and how these might be addressed.

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

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.

Reproducibility of COVID-19 pre-prints

To examine the reproducibility of COVID-19 research, we create a dataset of pre-prints posted to arXiv, bioRxiv, and medRxiv between 28 January 2020 and 30 June 2021 that are related to COVID-19. We extract the text from these pre-prints and parse them looking for keyword markers signaling the availability of the data and code underpinning the pre-print. For the pre-prints that are in our sample, we are unable to find markers of either open data or open code for 75% of those on arXiv, 67% of those on bioRxiv, and 79% of those on medRxiv.