Abstract
Generalization is inference from the specific circumstances of a clinical trial to other settings or populations with the condition of interest. Accomplishing this is complex because trials are not population samples, methods supporting both internal and external validity must be assessed, the trial data must be fit for purpose, and relevant shared biology must be a foundation for extrapolation of results. In the context of the large-scale randomized evidence from the COVID-19 vaccine trials, this chapter discusses these issues and how generalizations might be enhanced. Laboratory experiments are a useful microcosm of the same issues and carry important lessons for this process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baden LR, El Sahly HM, Essink B et al (2020) Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N Engl J Med 384(5):403–416
Benbrook CM (2019) How did the US EPA and IARC reach diametrically opposed conclusions on the genotoxicity of glyphosate-based herbicides? Environ Sci Eur 31:2. https://doi.org/10.1186/s12302-018-0184-7
Conn PM (2017) Animal models for the study of human disease, 2nd edn. Academic Press
Deer B (2020) The doctor who fooled the world : Andrew Wakefield’s war on vaccines. Johns Hopkins University Press, Baltimore
EPA (2017) Revised glyphosate issue paper: evaluation of carcinogenic potential. https://cfpub.epa.gov/si/si_public_file_download.cfm?p_download_id=534487
FDA (2018) Framework for FDA’s real-world evidence program. https://www.fda.gov/media/120060/download
Fukushima S et al (2015) Qualitative and quantitative approaches in the dose–response assessment of genotoxic carcinogens. Mutagenesis 31(3):341–346
Gamerman V, Cai T, Elsäßer A (2019) Pragmatic randomized clinical trials: best practices and statistical guidance. Health Serv Outcomes Res Methodol 19(1):23–35. https://doi.org/10.1007/s10742-018-0192-5
Guyton KZ et al (2015) Carcinogenicity of tetrachlorvinphos, parathion, malathion, diazinon, and glyphosate. Lancet Oncol 16(5):490–491
International Agency for Research on Cancer (2017) Some organophosphate insecticides and herbicides. IARC monographs on the evaluation of carcinogenic risks to humans, vol 112. https://publications.iarc.fr/549
ISIS-2 (Second International Study of Infarct Survival) Collaborative Group (1988) Randomised trial of intravenous streptokinase, oral aspirin, both, or neither among 17,187 cases of suspected acute myocardial infarction: ISIS-2. Lancet 2(8607):349–360
Patsopoulos NA (2011) A pragmatic view on pragmatic trials. Dial Clin Neurosci 13(2):217–224. https://doi.org/10.31887/DCNS.2011.13.2/npatsopoulos
Polack FP, Thomas SJ, Kitchin N et al (2020) Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med 383(27):2603–2615. https://doi.org/10.1056/NEJMoa2034577
Poulson RS, Gadbury GL, Allison DB (2012) Treatment heterogeneity and individual qualitative interaction. Am Statist 66(1):16–24. https://doi.org/10.1080/00031305.2012.671724
Rothenberg J (1989) The nature of modeling. RAND Corporation. https://www.rand.org/pubs/notes/N3027.html
Sadoff J, Gray G, Vandebosch A et al (2021) Safety and efficacy of single-dose Ad26.COV2.S vaccine against Covid-19. N Engl J Med 384(23):2187–2201. https://doi.org/10.1056/NEJMoa2101544
Sherman RE et al (2016) Real-world evidence – what is it and what can it tell us? N Engl J Med 375:2293–2297. https://doi.org/10.1056/NEJMsb1609216
Stackhouse CT, Gillespie GY, Willey CD (2019) Cancer explant models. In: Current topics in microbiology and immunology. Springer, Berlin, Heidelberg
U.S. Environmental Protection Agency (2021) Glyphosate. https://www.epa.gov/ingredients-used-pesticide-products/glyphosate
Wang X, Piantadosi S, Le-Rademacher J, Mandrekar S (2020) Statistical considerations for subgroup analyses. J Thor Onc 16(3):375–380. https://doi.org/10.1016/j.jtho.2020.12.008
Weber M (2012) Experiment in biology. In: Zalta EN (ed) The Stanford encyclopedia of philosophy. The Metaphysics Research Lab, Center for the Study of Language and Information, Stanford University, Stanford. http://plato.stanford.edu/entries/biology-experiment/
Weber M (2014) Experimental modeling in biology: in vivo representation and stand-ins as modeling strategies. Phil Sci 81(5):756–769. https://doi.org/10.1086/678257
Widman L, Loparo K, Nielsen N (1989) Artificial intelligence, simulation & modeling. Wiley
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this entry
Cite this entry
Piantadosi, S. (2022). Issues in Generalizing Results from Clinical Trials. In: Piantadosi, S., Meinert, C.L. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52636-2_236
Download citation
DOI: https://doi.org/10.1007/978-3-319-52636-2_236
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52635-5
Online ISBN: 978-3-319-52636-2
eBook Packages: Mathematics and StatisticsReference Module Computer Science and Engineering