Journal of Clinical Epidemiology
Volume 59, Issue 7 , Pages 685-696 , July 2006

A new preference-based analysis for randomized trials can estimate treatment acceptability and effect in compliant patients

  • S.D. Walter

      Affiliations

    • Department of Clinical Epidemiology and Biostatistics, McMaster University, HSC-2C16, 1200 Main St West, Hamilton, Ontario, L8N 3Z5 Canada
    • Corresponding Author InformationCorresponding author. Tel.: 905-525-9140, ext. 23387.
  • ,
  • Gordon Guyatt

      Affiliations

    • Department of Clinical Epidemiology and Biostatistics, McMaster University, HSC-2C16, 1200 Main St West, Hamilton, Ontario, L8N 3Z5 Canada
    • Department of Medicine, McMaster University, 1200 Main St West, Hamilton, Ontario, Canada
  • ,
  • Victor M. Montori

      Affiliations

    • Department of Medicine, Mayo Clinic College of Medicine, Mayo E17-96, 200 First St SW, Rochester, MN, 55905-0001, USA
  • ,
  • R. Cook

      Affiliations

    • Department of Statistics and Actuarial Science, University of Waterloo, 200 University Ave, Waterloo, Ontario, N2L 3G1 Canada
  • ,
  • K. Prasad

      Affiliations

    • Department of Neurology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, PIN-110029, India

,Accepted 15 November 2005.

References 

  1. White IR, Babiker AG, Walker S, Darbyshire JH. Randomization methods for correcting for treatment changes. Stat Med. 1999;18:2617–2634
  2. Goetghebeur E, Loeys T. Beyond intention to treat. Epidemiol Rev. 2002;24:85–90
  3. Yusuf S, Wittes J, Probstfield J, Tyroller HA. Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials. JAMA. 1991;266:93–98
  4. Lee J, Ellenberg J, Hirtz D, et al. Analysis of clinical trials by treatment actually received: is it really an option?. Stat Med. 1991;10:1595–1605
  5. White IR, Dunn G. Adjustment for non-compliance in randomized controlled trials. In:  Everitt BS,  Palmer CR editor. Encyclopedic companion to medical statistics. London: Hodder; 2004;
  6. DiMatteo MR, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes. Med Care. 2002;40:794–811
  7. Greenland S. An introduction for instrumental variables for epidemiologists. Int J Epidemiol. 2000;29:722–729
  8. White IR. Uses and limitations of randomization-based efficacy estimators. Stat Methods Med Res. 2005;14:327–347
  9. Canadian Lung Oncology Group . Investigation for mediastinal disease in patients with apparently operable lung cancer. Ann Thoracic Surg. 1995;60:1382–1389
  10. Barnett HJM NASCET collaborators. Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. N Engl J Med. 1991;325:445–453
  11. Cuzick J, Edwards R, Segnan N. Adjusting for non-compliance and contamination in randomized clinical trials. Stat Med. 1997;16:1017–1029
  12. McIntosh MW. Instrumental variables when evaluating screening trials. Stat Med. 1999;18:2775–2794
  13. Etzioni RD, Connor RJ, Prorock PC, Self SG. Design and analysis of cancer screening trials. Stat Methods Med Res. 1995;4:3–17
  14. Scheiner LB. Is intent-to-treat analysis always (ever) enough?. Br J Clin Pharmacol. 2002;54:203–211
  15. Sommer A, Zeger SL. On estimating efficacy from clinical trials. Stat Med. 1991;10:45–52
  16. Robins JM. Correction for non-compliance in equivalence trials. Stat Med. 1998;17:269–302
  17. Kenna LA, Scheiner LB. Estimating treatment effect in the presence of non-compliance measured with error. Stat Med. 2004;23:3561–3580
  18. Dunn G. The problem of measurement error in modelling the effect of compliance in a randomized clinical trial. Stat Med. 1999;18:2863–2877
  19. Mealli F, Imbens GW, Ferro S, Biggeri A. Analysing a randomized trial on breast self-examination with noncompliance and missing outcomes. Biostatistics. 2004;5:207–222
  20. Newcombe RG. Explanatory and pragmatic estimates of the treatment effect when deviations from allocated treatment occur. Stat Med. 1988;7:1179–1186
  21. King M, Nazareth I, Lampe F, Bower P, et al. Impact of participant and physician intervention preferences on randomized trials. JAMA. 2005;293:1089–1099
  22. Schmid CH, Lau J, McIntosh MW, Cappelleri JC. An empirical study of the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials. Stat Med. 1998;17:1923–1942
  23. Furukawa TA, Guyatt GH, Griffith LE. Can we individualize the Number Needed to Treat (NNT)? An empirical study of summary effect measures in meta-analyses. Int J Epidemiol. 2002;31:72–76
  24. Hampton JR, Henderson RA, Julian DG, Parker J, et al. Coronary angioplasty versus coronary artery bypass surgery: the Randomized Intervention Treatment of Angina (RITA) trial. Lancet. 1993;341:573–580
  25. CABRI Trial Participants . First-year results of CABRI (Coronary Angioplasty versus Bypass Revascularisation Investigation). Lancet. 1995;346:1179–1184
  26. BARI Investigators . Comparison of coronary bypass surgery with angioplasty in patients with multivessel disease. N Engl J Med. 1996;335:217–225
  27. Murphy ML, Hultgren HN, Detre K, Thomsen J, et al. Treatment of chronic stable angina. N Engl J Med. 1977;297:621–627

PII: S0895-4356(06)00003-5

doi: 10.1016/j.jclinepi.2005.11.016

Journal of Clinical Epidemiology
Volume 59, Issue 7 , Pages 685-696 , July 2006