The Role of Statistical Thinking in Biopharmaceutical Research

References

• Baillie, M., Moloney, C., Mueller, C. P., Dorn, J., Branson, J., and Ohlssen, D. (2022), “Good Data Science Practice: Moving toward a Code of Practice for Drug Development,” Statistics in Biopharmaceutical Research, 15, 74–85. DOI: 10.1080/19466315.2022.2063172.
   Web of Science ®Google Scholar
• Blei, D. M., and Smyth, P. (2017), “Science and Data Science,” Proceedings of the National Academy of Sciences of the United States of America, 114, 8689–8692. DOI: 10.1073/pnas.1702076114.
   PubMed Web of Science ®Google Scholar
• Bornkamp, B., Zaoli, S., Azzarito, M., Martin, R., Müller, C. P., Moloney, C., Capestro, G., Ohlssen, D., and Baillie, M. (2023), “Predicting Subgroup Treatment Effects for a New Study: Motivations, Results and Learnings from Running a Data Challenge in a Pharmaceutical Corporation,” manuscript available at https://arxiv.org/abs/2304.05658.
   Google Scholar
• Boulesteix, A. L., Lauer, S., and Eugster, M. J. A. (2013), “A Plea for Neutral Comparison Studies in Computational Sciences,” PloS One, 8, e61562. DOI: 10.1371/journal.pone.0061562.
   PubMed Web of Science ®Google Scholar
• Box, G. E. P. (1976), “Science and Statistics,” Journal of the American Statistical Association, 71, 791–799. DOI: 10.1080/01621459.1976.10480949.
   Web of Science ®Google Scholar
• Bransford, J. D., Brown, A. L., and Cocking, R. R., eds. (1999), How People Learn: Brain, Mind, Experience and School, Washington, D.C.: National Academy Press.
   Google Scholar
• Bretz, F., and Xi, D. (2019), “Discussion of “LaVange LM (2019) Statistics at FDA: Reflections on the past Six Years”,” Statistics in Biopharmaceutical Research, 11, 20–25. DOI: 10.1080/19466315.2018.1562369.
   Web of Science ®Google Scholar
• Cardner, M., Tuckwell, D., Kostikova, A., Forrer, P., Siegel, R., and Marti, A., et al. (2022), “Analysis of Serum Proteomics Data Identifies Beta-Defensin 2 as a Predictive Biomarker of Clinical Response to IL-17 Blockade in Psoriatic Arthritis,” Abstract available at https://acrabstracts.org/abstract/analysis-of-serum-proteomics-data-identifies-beta-defensin-2-as-a-predictive-biomarker-of-clinical-response-to-il-17-blockade-in-psoriatic-arthritis/.
   Google Scholar
• Cox, D. R. (2012), “Comment on “Cornfield J (2012) Principles of Research”,” Statistics in Medicine, 31, 2770. DOI: 10.1002/sim.5375.
   PubMed Web of Science ®Google Scholar
• Docherty, M., Regnier, S. A., Capkun, G., Balp, M.-M., Ye, Q., Janssens, N., Tietz, A., Löffler, J., Cai, J., Pedrosa, M. C., and Schattenberg, J. M. (2021), “Development of a Novel Machine Learning Model to Predict Presence of Nonalcoholic Steatohepatitis,” Journal of the American Medical Informatics Association: JAMIA, 28, 1235–1241. DOI: 10.1093/jamia/ocab003.
   PubMed Web of Science ®Google Scholar
• Donoho, D. (2017), “50 Years of Data Science,” Journal of Computational and Graphical Statistics, 26, 745–766. DOI: 10.1080/10618600.2017.1384734.
   Web of Science ®Google Scholar
• Dunn, R., Greenhouse, J., James, D., Ohlssen, D., and Mesenbrink, P. (2020), “Risk Scoring for Time to End-Stage Knee Osteoarthritis: Data from the Osteoarthritis Initiative,” Osteoarthritis and Cartilage, 28, 1020–1029. DOI: 10.1016/j.joca.2019.12.013.
   PubMedGoogle Scholar
• Efron, B., and Hastie, T. (2016), Computer Age Statistical Inference, Cambridge: Cambridge University Press.
   Google Scholar
• FDA (2022), “FDA Approves alpelisib for PIK3CA-Related Overgrowth Spectrum,” available at https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-alpelisib-pik3ca-related-overgrowth-spectrum.
   Google Scholar
• Fisher, R. A. (1922), “On the Mathematical Foundations of Theoretical Statistics,” Philosophical Transactions of Royal Society of London – Series A, 222, 309–368.
   Google Scholar
• Gibson, E. W. (2019), “Leadership in Statistics: Increasing Our Value and Visibility,” The American Statistician, 73, 109–116. DOI: 10.1080/00031305.2017.1336484.
   Web of Science ®Google Scholar
• Gibson, E. W. (2021), “The Role of p-Values in Judging the Strength of Evidence and Realistic Replication Expectations,” Statistics in Biopharmaceutical Research, 13, 6–18. DOI: 10.1080/19466315.2020.1724560.
   Web of Science ®Google Scholar
• Greenhouse, J. B., and Seltman, H. J. (2018), “On Teaching Statistical Practice: From Novice to Expert,” The American Statistician, 72, 147–154. DOI: 10.1080/00031305.2016.1270230.
   Web of Science ®Google Scholar
• Greenhouse, J. B., Stangl, D., Kupfer, D. J., and Prien, R. F. (1991), “Methodological Issues in Maintenance Therapy Clinical Trials,” Archives of General Psychiatry, 48, 313–318. DOI: 10.1001/archpsyc.1991.01810280029004.
   PubMed Web of Science ®Google Scholar
• Grolemund, G., and Wickham, H. (2014), “A Cognitive Interpretation of Data Analysis,” International Statistical Review, 82, 184–204. DOI: 10.1111/insr.12028.
   Web of Science ®Google Scholar
• Guthridge, J. M., Wagner, C. A., and James, J. A. (2022), “The Promise of Precision Medicine in Rheumatology,” Nature Medicine, 28, 1363–1371. DOI: 10.1038/s41591-022-01880-6.
   PubMed Web of Science ®Google Scholar
• Hengartner, M. P., and Plöderl, M. (2021), “Prophylactic Effects or Withdrawal Reactions? An Analysis of Time-to-Event Data from Antidepressant Relapse Prevention Trials Submitted to the FDA,” Therapeutic Advances in Psychopharmacology, 11, 1–11. DOI: 10.1177/20451253211032051.
   Web of Science ®Google Scholar
• Hernan, M. A., and Robins, J. M. (2023), Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.
   Google Scholar
• ICH (2019), “International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use,” E9(R1) Guideline: Estimands and Sensitivity Analysis in Clinical Trials. Available at https://www.ich.org
   Google Scholar
• Lipkovich, I., Ratitch, B., and Mallinckrodt, C. H. (2020), “Causal Inference and Estimands in Clinical Trials,” Statistics in Biopharmaceutical Research, 12, 54–67. DOI: 10.1080/19466315.2019.1697739.
   Web of Science ®Google Scholar
• MacKay, R. J., and Oldford, R. W. (2000), “Scientific Method, Statistical Method and the Speed of Light,” Statistical Science, 15, 254–278. DOI: 10.1214/ss/1009212817.
   Web of Science ®Google Scholar
• Mallows, C. (1998), “The Zeroth Problem,” The American Statistician, 52, 1–9. DOI: 10.2307/2685557.
   Web of Science ®Google Scholar
• Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., and Collins, G. S. (2015), “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration,” Annals of Internal Medicine, 162, W1–W73. DOI: 10.7326/M14-0698.
   PubMed Web of Science ®Google Scholar
• O’Connell, P., Ridolfi, A., and Fretault, N. (2023), “Case Study Using RWD in the Context of a Pivotal Trial for Regulatory Approval in a Rare Disease,” Journal of Biopharmaceutical Statistics. Published online. DOI: 10.1080/10543406.2023.2170406.
   PubMed Web of Science ®Google Scholar
• Prien, R. F., Kupfer, D. J., Mansky, P. A., Small, J. G., Tuason, V. B., Voss, C. B., and Johnson, W. E. (1984), “Drug Therapy in the Prevention of Recurrences in Unipolar and Bipolar Affective Disorders,” Archives of General Psychiatry, 41, 1096–1104. DOI: 10.1001/archpsyc.1983.01790220086014.
   PubMed Web of Science ®Google Scholar
• Rockhold, F. W. (2000), “Strategic Use of Statistical Thinking in Drug Development,” Statistics in Medicine, 19, 3211–3217. DOI: 10.1002/1097-0258(20001215)19:23<3211::AID-SIM622>3.0.CO;2-F.
   PubMed Web of Science ®Google Scholar
• Sylwester, D. (1993), “Statistical Thinking,” AMSTAT News, February.
   Google Scholar
• Taylor-Weiner, A., Pokkalla, H., Han, L., Jia, C., Huss, R., Chung, C., Elliott, H., Glass, B., Pethia, K., Carrasco-Zevallos, O., Shukla, C., Khettry, U., Najarian, R., Taliano, R., Subramanian, G. M., Myers, R. P., Wapinski, I., Khosla, A., Resnick, M., Montalto, M. C., Anstee, Q. M., Wong, V. W.-S., Trauner, M., Lawitz, E. J., Harrison, S. A., Okanoue, T., Romero-Gomez, M., Goodman, Z., Loomba, R., Beck, A. H., and Younossi, Z. M. (2021), “A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH,” Hepatology (Baltimore, Md.), 74, 133–147. DOI: 10.1002/hep.31750.
   PubMed Web of Science ®Google Scholar
• Wasserstein, R. L., Schirm, A. L., and Lazar, N. A. (2019), “Moving to a World beyond “p < 0.05,” The American Statistician, 73, 1–19.
   Web of Science ®Google Scholar
• Wild, C. J., and Pfannkuch, M. (1999), “Statistical Thinking in Empirical Enquiry” (with discussion), International Statistical Review/Revue Internationale de Statistique, 67, 223–248. DOI: 10.2307/1403699.
   Web of Science ®Google Scholar
• Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., ‘t Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B. (2016), “The FAIR Guiding Principles for Scientific Data Management and Stewardship,” Scientific Data, 3, 160018. DOI: 10.1038/sdata.2016.18.
   PubMed Web of Science ®Google Scholar