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Methodological Foundations of Clinical Research

  • Antonella BacchieriEmail author
  • Giovanni Della Cioppa
Chapter
Part of the Health Informatics book series (HI)

Abstract

This chapter focuses on clinical experiments, discussing the phases of the pharmaceutical development process. We review the conceptual framework and classification of biomedical studies and look at their distinctive characteristics. Biomedical studies are classified into two main categories, observational and experimental, which are then further classified into subcategories of prospective and retrospective and community and clinical, respectively. We review the basic concepts of experimental design, including defining study samples and calculating sample size, where the sample is the group of subjects on which the study is performed. Choosing a sample involves both qualitative and quantitative considerations, and the sample must be representative of the population under study. We then discuss treatments, including those that are the object of the experiment (study treatments) and those that are not (concomitant treatments). Minimizing bias through the use of randomization, blinding, and a priori definition of the statistical analysis is also discussed. Finally, we briefly look at innovative approaches, for example, how adaptive clinical trials can shorten the time and reduce the cost of classical research programs or how targeted designs can allow a more efficient use of patients in rare conditions.

Keywords

Phase I, II, III, and IV trials Classification of biomedical studies Observational study Experimental study Equivalence/non-inferiority studies Superiority versus non-inferiority studies Crossover designs Parallel group designs Adaptive clinical trials Targeted designs 

References

  1. 1.
    Bacchieri A, Della Cioppa G. Fundamentals of clinical research. Bridging medicine, statistics and operations. Milan: Springer; 2007.CrossRefGoogle Scholar
  2. 2.
    Hill RG, Rang HP, editors. Drug discovery and development. 2nd ed. Churchill Livingstone: Elsevier; 2012.Google Scholar
  3. 3.
    DiMasi J, Hansen R, Gabrowski H. The price of innovation: new estimates of drug development cost. J Health Econ. 2003;22:151–8.CrossRefGoogle Scholar
  4. 4.
    Lilienfeld AM, Lilienfeld DE. Foundations of epidemiology. 2nd ed. New York: Oxford University Press; 1980.Google Scholar
  5. 5.
  6. 6.
    Pretince R. Surrogate end-points in clinical trials: definition and operational criteria. Stat Med. 1989;8:431–40.CrossRefGoogle Scholar
  7. 7.
    Bland JM, Altman DG. Regression toward the mean. BMJ. 1994;308:1499.CrossRefGoogle Scholar
  8. 8.
    Bland JM, Altman DG. Some examples of regression toward the mean. BMJ. 1994;309:780.CrossRefGoogle Scholar
  9. 9.
    http://www.rethinkingclinicaltrials.org/. Living textbook of pragmatic clinical trials.
  10. 10.
    Armitage P. Sequential medical trials. Blackwell Scientific Publications. Oxford: London; 1975.Google Scholar
  11. 11.
    Pocock SJ. Group sequential methods in the design and analysis of clinical trials. Biometrika. 1977;64(2):191–9.CrossRefGoogle Scholar
  12. 12.
    O’Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics. 1979;35(3):549–56.CrossRefGoogle Scholar
  13. 13.
    Demets DL, Lan KG. Interim analysis: the alpha spending approach. Stat Med. 1994;13(13–14):1341–52.CrossRefGoogle Scholar
  14. 14.
    Chow SC, Chang M. Adaptive design methods in clinical trials – a review. Orphanet J Rare Dis. 2008;3:11.  https://doi.org/10.1186/1750-1172-3-11.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Meurer WJ, Lewis RJ, Berry DA. Adaptive clinical trials: a partial remedy for the therapeutic misconception? JAMA. 2012;307(22):2377–8.CrossRefGoogle Scholar
  16. 16.
    Bauer P, Kohne K. Evaluation of experiments with adaptive interim analyses. Biometrics. 1994;50:1029–41.CrossRefGoogle Scholar
  17. 17.
    Jennison C, Tumbull BW. Mid-course sample size modification in clinical trials based on the observed treatment effect. Stat Med. 2003;22:971–93.CrossRefGoogle Scholar
  18. 18.
    Proscham M, Liu Q, Hunsberger S. Practical mid-course sample size modification in clinical trials. Control Clin Trials. 2003;24:4–15.CrossRefGoogle Scholar
  19. 19.
    Shun Z. Sample size re-estimation in clinical trials. Drug Inf J. 2001;35:1409–22.CrossRefGoogle Scholar
  20. 20.
    Gould AL. Sample size re-estimation: recent developments and practical considerations. Stat Med. 2001;20:2625–43.CrossRefGoogle Scholar
  21. 21.
    Lin J, Lin LA, Sankoh S. A general overview of adaptive randomization design for clinical trials. J Biom Biostat. 2016;7:2.  https://doi.org/10.4172/2155-6180.1000294.CrossRefGoogle Scholar
  22. 22.
    Hu F, Rosenberger WF. The theory of response-adaptive randomization in clinical trials. Hoboken: Wiley. 2006CrossRefGoogle Scholar
  23. 23.
    Thall PF, Wathen JK. Practical Bayesian adaptive randomization in clinical trials. Eur J Cancer. 2007;43:859–66.CrossRefGoogle Scholar
  24. 24.
    Iasonos A, O’Quigley J. Adaptive dose-finding studies: a review of model-guided phase I clinical trials. J Clin Oncol. 2014;32(23):2505–11.CrossRefGoogle Scholar
  25. 25.
    O’Quigley J, Pepe M, Fisher L. Continual reassessment method: a practical design for phase I clinical trials in cancer. Biometrics. 1990;46(1):33–48.CrossRefGoogle Scholar
  26. 26.
  27. 27.
    Gaydos B, Krams M, Perevozskaya I, et al. Adaptive dose-response studies. Drug Inf J. 2006;40:451–61.CrossRefGoogle Scholar
  28. 28.
    Bauer P, Rohmel J. An adaptive method for establishing a dose-response relationship. Stat Med. 1995;14:1595–607.CrossRefGoogle Scholar
  29. 29.
    Maca J, Bhattacharya S, Dragalin V, et al. Adaptive seamless phase II/III designs. Background, operational aspects and examples. Drug Inf J. 2006;40:463–73.CrossRefGoogle Scholar
  30. 30.
    Liu Q, Pledger GW. Phase 2 and 3 combination designs to accelerate drug development. J Am Stat Assoc. 2005;100:493–502.CrossRefGoogle Scholar
  31. 31.
    Era 21Liu Q, Proscham MA, Pledger GW. A unified theory of two-stage adaptive designs. J Am Stat Soc. 2002;97:1034–41.CrossRefGoogle Scholar
  32. 32.
    Era 22Bauer P, Kieser M. Combining different phases in the development of medical treatments within a single trial. Stat Med. 1999;18:1833–48.CrossRefGoogle Scholar
  33. 33.
    Don GA. A varying-stage adaptive phase II/III clinical trial design. Stat Med. 2014;33:1272–87.CrossRefGoogle Scholar
  34. 34.
    Branson M, Whitehead J. Estimating a treatment effect in survival studies in which patients switch treatment. Stat Med. 2002;21(17):2449–63.CrossRefGoogle Scholar
  35. 35.
    Hommel G. Adaptive modifications of hypotheses after an interim analysis. Biom J. 2001;43:581–9.CrossRefGoogle Scholar
  36. 36.
    Muller HH, Schafer H. A general statistical principle for changing a design any time during the course of a trial. Stat Med. 2004;23:2497–508.CrossRefGoogle Scholar
  37. 37.
    Biankin AV, Piantadosi S, Hollingsworth SJ. Patient-centric trials for therapeutic development in precision oncology. Nature. 2015;526:361–70.CrossRefGoogle Scholar
  38. 38.
    Chen C, Li X, Yuan S, Antonijevic Z, Kalamegham R, Beckman RA. Statistical design and considerations of a phase III basket trial for simultaneous investigation of multiple tumor types in one study. Stat Biopharm Res. 2016;8(3):248–57.CrossRefGoogle Scholar
  39. 39.
    Cunanan KM, Gonen M, Shen R, Hyman DM, Riely GI, Begg CB, Iasonos A. Basket trials in oncology: a trade-off between complexity and efficiency. J Clin Oncol. 2017;35(3):271–3.CrossRefGoogle Scholar
  40. 40.
    Cunanan KM, Iasonos A, Shen R, Hyman D, Begg CB, Gonen M. An efficient basket trial design. Stat Med. 2017;36(10):1568–79.PubMedPubMedCentralGoogle Scholar
  41. 41.
    Berry SM, Connor JT, Lewis RJ. The platform trial: an efficient strategy for evaluating multiple treatments. JAMA. 2015;313(16):1619–20.CrossRefGoogle Scholar
  42. 42.
    Saville BR, Berry SM. Efficiencies of platform clinical trials: a vision of the future. Clin Trials. 2016;13(3):358–66.CrossRefGoogle Scholar
  43. 43.
    Hussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials. 2007;28:182–91.CrossRefGoogle Scholar
  44. 44.
    Hemming K, et al. The stepped wedge cluster randomized trial: rationale, design, analysis, and reporting. BMJ. 2015;h391:351.Google Scholar
  45. 45.
    Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov. 2004;3:711–6.CrossRefGoogle Scholar
  46. 46.
    Cummings IL, Morstorf T, Zhong K. Alzheimer’s disease drug-development pipeline: few candidates, frequent failure. Alzheimers Res Ther. 2014;6(4):37.CrossRefGoogle Scholar
  47. 47.
    Minnerup J, Wersching H, Schilling M, Schabitz WR. Analysis of early phase and subsequent phase III stroke studies of neuroprotectants outcomes and predictor for success. Exp Transl Stroke Med. 2014;6(1):2.CrossRefGoogle Scholar
  48. 48.
    Sacks LV, et al. Scientific and regulatory reasons for delay and denial of FDA approval of initial applications for new drugs, 2000–2012. JAMA. 2014;311:378–84.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing 2019

Authors and Affiliations

  1. 1.CROS NT srl and Clinical R&D Consultants srlsVeronaItaly
  2. 2.Clinical R&D Consultants srlsRomeItaly

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