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Individual Therapy: New Dawn or False Dawn?

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Abstract

The sequencing of the human genome brings with it the hope that greater understanding of genetic components of disease will allow the more specific targeting of therapies. It has also been suggested that it will permit sponsors to run “cleaner” clinical trials with less variability and a consequent saving in patient numbers. However, we do not know how much of the variation in response that we see from patient to patient in clinical trials is genetic, because we rarely design the sort of trials that would allow us to identify patient-by-treatment interaction. Such interaction provides an upper bound for gene-by-treatment interaction for a group of patients studied since patients differ by more than their genes. On the other hand, however, the variability seen within a clinical trial may generally be expected to be less than the total variation that would be seen within a population. There is a related statistical issue to do with the interpretation of effects from clinical trials. This arises because there is confusion between experimental and sampling models of clinical research. It is concluded that we may have to pay careful attention to certain design features of clinical trials if we wish to make progress in this field.

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References

  1. Sykes R. The Pharmaceutical Industry in the New Millenium: Capturing the Scientific Promise. London: Centre for Medicines Research; 1977, 1–28.

    Google Scholar 

  2. Evans WE, Relling MV. Pharmacogenomics: Translating functional genomics into rational therapeutics. Science. 1999;286:487–491.

    Article  CAS  PubMed  Google Scholar 

  3. Conover WJ, Salsburg DS. Locally most powerful tests for detecting treatment effects when only a subset of patients can be expected to respond to treatment. Biometrics. 1988;44:189–196.

    Article  CAS  PubMed  Google Scholar 

  4. Sheiner LB, Rosenberg B, Melmon KL. Modelling of individual pharmacokinetics for computer-aided drug dosage. Comput Biomed Res. 1972;5:411–459.

    Article  CAS  PubMed  Google Scholar 

  5. Hartl DL. A Primer of Population Genetics. Sunderland, MA: Sinauer Associates, Inc; 2000.

    Google Scholar 

  6. Elston RC. Introduction and overview. Stat Methods Med Res. 2000;9:527–541.

    Article  CAS  PubMed  Google Scholar 

  7. Horwitz RI, Singer BH, Makuch RW, Viscoli CM. Can treatment that is helpful on average be harmful to some patients? A study of the conflicting information needs of clinical inquiry and drug regulation [see comments). J Clin Epidemiol. 1996;49:395–400.

    Article  CAS  PubMed  Google Scholar 

  8. Gail M, Simon R. Testing for qualitative interactions between treatment effects and patient subsets. Biometrics. 1985;41:361–372.

    Article  CAS  PubMed  Google Scholar 

  9. Senn SJ, Harrell F. On wisdom after the event [comment] [see comments]. J Clin Epidemiol. 1997;50: 749–751.

    Article  CAS  PubMed  Google Scholar 

  10. Senn SJ, Harrell FE Jr. On subgroups and groping for significance [letter; comment]. J Clin Epidemiol. 1998;51:1367–1368.

    Article  CAS  PubMed  Google Scholar 

  11. Horwitz RI, Singer BH, Makuch RW, Viscoli CM. On reaching the tunnel at the end of the light [comment] [see comments]. J Clin Epidemiol. 1997;50: 753–755.

    Article  CAS  PubMed  Google Scholar 

  12. Galbraith RF. Graphical display of estimates having differing standard errors. Technometrics. 1988;30: 271–281.

    Article  Google Scholar 

  13. Galbraith RF. A note on graphical presentation of estimated odds ratios from several clinical-trials. Stat Med. 1988;7:889–894.

    Article  CAS  PubMed  Google Scholar 

  14. Galbraith RF. Some applications of radial plots. J Am Stat Assoc. 1994;89:1232–1242.

    Article  Google Scholar 

  15. Hardy RJ, Thompson SG. A likelihood approach to meta-analysis with random effects. Stat Med. 1996; 15:619–629.

    Article  CAS  PubMed  Google Scholar 

  16. Senn SJ. Statistical Issues in Drug Development. Chichester: John Wiley; 1997.

    Google Scholar 

  17. Guyatt GH, Juniper EF, Walter SD, Griffith LE, Goldstein RS. Interpreting treatment effects in ran-domised trials [see comments]. Br Med J. 1998;316: 690–693.

    Article  CAS  Google Scholar 

  18. Hutton JL. Numbers needed to treat: properties and problems (with comments). J Roy Stat Society A. 2000;163:403–419.

    Google Scholar 

  19. Smeeth L, Haines A, Ebrahim S. Numbers needed to treat derived from meta-analyses—sometimes informative, usually misleading [see comments]. Br Med J. 1999;318:1548–1551.

    Article  CAS  Google Scholar 

  20. Senn SJ. Applying results of randomised trials to patients. N of 1 trials are needed [letter; comment]. Br Med J. 1998;317:537–538.

    Article  CAS  Google Scholar 

  21. Cox DR, Fitzpatrick R, Fletcher AE, Gore SM, Spiegelhalter DJ, Jones DR. Quality-of-life assessment—Can we keep it simple. J R Stat Soc Ser A-Stat Soc. 1992;155:353–393.

    Article  Google Scholar 

  22. March L, Irwig L, Schwarz J, Simpson J, Chock C, Brooks P. n of 1 trials comparing a non-steroidal anti-inflammatory drug with paracetamol in osteoarthritis. Br Med J. 1994;309:1041–1045; discussion 1045-1046.

    Article  CAS  Google Scholar 

  23. Senn SJ. Suspended judgment n-of-1 trials. Control Clin Trials. 1993;14:1–5.

    Article  CAS  PubMed  Google Scholar 

  24. Senn SJ, Bakshi R, Ezzet N. n of 1 trials in osteoarthritis. Caution in interpretation needed [letter; comment]. Br Med J. 1995;310:667.

    Article  CAS  Google Scholar 

  25. Shumaker RC, Metzlcr, CM. The phenytoin trial is a case study of “individual bioequivalence.” Drug Inf J. 1998;32:1063–1072.

    Article  Google Scholar 

  26. Kieser M, Hauschke D. Statistical method for demonstrating equivalence in crossover trials based on the ratio of two location parameters. Drug Inf J. 2000;34:563–568.

    Article  Google Scholar 

  27. Senn SJ. In the blood: proposed new requirements for registering generic drugs. Lancet. 1998;352:85–86.

    Article  CAS  PubMed  Google Scholar 

  28. Kane GC, Lipsky JJ. Drug-grapefruit juice interactions. Mayo Clin Proc. 2000;75:933–942.

    Article  CAS  PubMed  Google Scholar 

  29. Kalow W, Tang BK, Endrenyi L. Hypothesis: comparisons of inter- and intra-individual variations can substitute for twin studies in drug research. Pharmacogenetics. 1998;8:283–289.

    Article  CAS  PubMed  Google Scholar 

  30. Kalow W, Ozdemir V, Tang BK, Tothfalusi L, Endrenyi L. The science of pharmacological variability: an essay. Clin Pharmacol Ther. 1999;66:445–447.

    Article  CAS  PubMed  Google Scholar 

  31. Ozdemir V, Kalowa W. Tang BK, Paterson AD, Walker SE, Endrenyi L, Kashuba AD. Evaluation of the genetic component of variability in CYP3A4 activity: a repeated drug administration method. Pharmacogenetics. 2000;10:373–388.

    Article  CAS  PubMed  Google Scholar 

  32. Senn SJ. Crossover Design. In: Encyclopedia of Bio-pharmaceutical Statistics. SC Chow and JP Liu, eds. New York: Marcel Dekker; 2000, 142–149.

    Google Scholar 

  33. Senn SJ. Cross-over trials. In: Encyclopedia in Bio-statistics., P Armitage and T Colton, eds. New York: Wiley; 1998, 1033–1049.

    Google Scholar 

  34. Senn SJ. Cross-over Trials in Clinical Research. Chichester: John Wiley; 1993.

    Google Scholar 

  35. Meinert CL, Gilpin AK, Unalp A, Dawson C. Gender representation in trials. Control Clin Trials. 2000;21:462–475.

    Article  CAS  PubMed  Google Scholar 

  36. Rom DM, Hwang E. Testing for individual and population equivalence based on the proportion of similar responses [see comments]. Stat Med. 1996:15:1489–1505.

    Article  CAS  PubMed  Google Scholar 

  37. Senn SJ. Testing for individual and population equivalence based on the proportion of similar responses [letter; comment]. Stat Med. 1997;16:1303–1306.

    Article  CAS  PubMed  Google Scholar 

  38. Hauck WW, Hyslop T, Anderson S. Generalized treatment effects for clinical trials. Slat Med. 2000; 19:887–899.

    CAS  Google Scholar 

  39. Chen M, Kianifard F. A nonparametric procedure associated with a clinically meaningful efficacy measure. Biostatistics. 2000;1:293–298.

    Article  CAS  PubMed  Google Scholar 

  40. Glass, GE. Primary, secondary and meta-analysis of research. Educat Research. 1976;5:3–8.

    Article  Google Scholar 

  41. Rosenbaum PR, Rubin DR. The central role of the propensity score in observational studies for causal effect. Biometrika. 1983;70:41–55.

    Article  Google Scholar 

  42. Rubin DB. Estimating causal effects of treatment in randomized and nonrandomized studies. J Educat Psychol. 1974;66:688–701.

    Article  Google Scholar 

  43. Meyer UA. Pharmacogenetics and adverse drug reactions. Lancet. 2000;356.

    Google Scholar 

  44. Guo X, Rotimi C, Cooper R, Luke A, Elston RC, Ogunbiyi O, Ward R. Evidence of a major gene effect for angiotensinogen among Nigerians. Ann Hum Genet. 1999;63:293–300.

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Departments of Epidemiology and Public Health and Statistical Science, University College London, London, UK

    Stephen Senn Ba, MSc, PhD (Professor of Pharmaceutical and Health Statistics)

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  1. Stephen Senn Ba, MSc, PhD
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Correspondence to Stephen Senn Ba, MSc, PhD.

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Senn, S. Individual Therapy: New Dawn or False Dawn?. Ther Innov Regul Sci 35, 1479–1494 (2001). https://doi.org/10.1177/009286150103500443

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