The Quest for Timely Insights into COVID-19 Should not Come at the Cost of Scientific Rigor
- PMID: 33065609
- DOI: 10.1097/EDE.0000000000001258
The Quest for Timely Insights into COVID-19 Should not Come at the Cost of Scientific Rigor
Comment in
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The Authors Respond.Epidemiology. 2021 Jan;32(1):e2-e3. doi: 10.1097/EDE.0000000000001260. Epidemiology. 2021. PMID: 33105269 No abstract available.
Comment on
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Factors associated with COVID-19-related death using OpenSAFELY.Nature. 2020 Aug;584(7821):430-436. doi: 10.1038/s41586-020-2521-4. Epub 2020 Jul 8. Nature. 2020. PMID: 32640463 Free PMC article.
References
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- Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584:430–436.
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- Arnold KF, Davies V, de Kamps M, Tennant PWG, Mbotwa J, Gilthorpe MS. Reflections on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning. Int J Epidemiol. 2020:dyaa049
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- Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. 2019.New York: Springer US;
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- Westreich D, van Smeden M, Edwards J. Response to GOLDACRE ET AL . (OpenSAFELY Collaborative). Zenodo. 2020DOI: 10.5281/zenodo.3855586 - DOI
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- Hernán MA. The C-word: scientific euphemisms do not improve causal inference from observational data. Am J Public Health. 2018;108:616–619.
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