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Special Section: A Collection of Articles on Opportunities and Challenges in Utilizing Real-World Data for Clinical Trials and Medical Product Development

Comment on “Good Data Science Practice: Moving towards a Code of Practice for Drug Development”

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Pages 86-88 | Received 23 May 2022, Accepted 24 May 2022, Published online: 18 Jul 2022

References

  • Baillie, M., Moloney, C., Mueller, C. P., Dorn, J., Branson, J., and Ohlssen, D. (2022), “Good Data Science Practice: Moving Towards a Code of Practice for Drug Development,” Statistics in Biopharmaceutical Research, this issue. DOI: 10.1080/19466315.2022.2063172.
  • FDA (2021a), “Real-World Data: Assessing Electronic Health Records and Medical Claims Data to Support Regulatory Decision-Making for Drug and Biological Products,” available at https://www.fda.gov/media/152503/download.
  • FDA (2021b), “Real-World Data: Assessing Registries to Support Regulatory Decision-Making for Drug and Biological Products,” available at https://www.fda.gov/media/154449/download.
  • Gelman, A., and Vehtari, A. (2021), “What are the Most Important Statistical Ideas of the Past 50 Years?,” Journal of the American Statistical Association, 116, 2087–2097. DOI: 10.1080/01621459.2021.1938081.
  • Hernan, M. A., and Robins, J. M. (2020), Causal Inference: What If, Boca Raton: Chapman & Hall/CRC.
  • ICH (2020), “ICH E9(R1) Addendum to Statistical Principles for Clinical Trials on Choosing Appropriate Estimands and Defining Sensitivity Analyses in Clinical Trials,” available at https://www.ich.org/page/efficacy-guidelines.
  • Pearl, J., Glymour, M., and Jewell, N. P. (2016), Causal Inference in Statistics: A Primer, Hoboken, NJ: Wiley.
  • Pearl, J., and Mackenzie, D. (2018), The Book of Why: The New Science of Cause and Effect, New York: Basic Books.
  • van der Laan, M. J., Polley, E. C., and Hubbard, A. E. (2007), “Super Learner,” Statistical Applications in Genetics and Molecular Biology, 6, 25. DOI: 10.2202/1544-6115.1309.
  • van der Laan, M. J., and Rose, S. (2011), Targeted Learning: Causal Inference for Observational and Experimental Data, New York: Springer.
  • van der Laan, M. J., and Rose, S. (2018), Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies, Cham: Springer.

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