Skip to main content
SpringerLink
Log in

Advanced Statistical Methods

  • Chapter
  • First Online:
Clinical Trials Design in Operative and Non Operative Invasive Procedures

  • 1385 Accesses

Abstract

With the development of medicine and data technologies, modern clinical trials are often situated to address sophisticated therapeutic questions that require advanced statistical techniques. This chapter introduces readers to several advanced statistical topics one may likely encounter in today’s clinical trials. These topics include multiple endpoints, subgroup analysis, site and operator heterogeneity, and time-to-event outcomes. Each topic will be covered in a separate subsection. The subsection on multiple endpoints, which are used to measure multiple aspects of a disease, includes a discussion of advanced multiplicity adjustment and composite endpoints construction. Subgroup analysis introduces methods to evaluate efficacy and conduct hypothesis testing in multiple subpopulations in addition to the overall population. Site and operator heterogeneity can be considered as a case of subgroup analysis of special importance for surgical procedures. Meta-analysis methods and mixed models are discussed. The time-to-event subsection introduces basic concepts of time-to-event derivation, censoring, and common analysis techniques such as Kaplan–Meier curve, proportional hazards regression, and restricted mean survival.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bagheri Z, Ayatollahi SMT, Jafari P. Comparison of three tests of homogeneity of odds ratios in multicenter trials with unequal sample sizes within and among centers. BMC Med Res Methodol. 2011;11:58.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Bauer P, Röhmel J, Maurer W, Hothorn L. Testing strategies in multi-dose experiments including active control. Stat Med. 1998;17:2133–46.

    Article  CAS  PubMed  Google Scholar 

  3. Belitskaya-Levy I, Wang H, Shih MC, Tian L, Doros G, Lew RA, Lu Y. A new overall-subgroup simultaneous test for optimal inference in biomarker-targeted confirmatory trials. Stat. Biosci. doi:10.1007/s12561-016-9174-8.

  4. Biosense Webster Inc. FDA executive summary prepared for the November 20, 2008 meeting of the Circulatory System Devices Panel P030031/S011 NaviStar ThermoCool RF Ablation Catheters. 2008. http://www.fda.gov/ohrms/dockets/ac/08/briefing/2008-4393b1-01%20%20FDA%20executive%20summary%20FINAL.pdf.

  5. Breslow NE, Day NE. Statistical methods in cancer research: volume 1—the analysis of case-control studies. Lyon, France: IARC Scientific Publications; 1980.

    Google Scholar 

  6. Burstein HJ. The distinctive nature of HER2-positive breast cancers. N Engl J Med. 2005;353(16):1652–4.

    Article  CAS  PubMed  Google Scholar 

  7. Cappuzzo F, Ciuleanu T, Stelmakh L, et al. Erlotinib as maintenance treatment in advanced non-small-cell lung cancer: a multicentre, randomised, placebo-controlled phase 3 study. Lancet Oncol. 2010;11:521–9.

    Article  CAS  PubMed  Google Scholar 

  8. Cochran WG. The combination of estimates from different experiments. Biometrics. 1954;10:101–29.

    Article  Google Scholar 

  9. Cochran WG. Some methods for strengthening the common χ2 tests. Biometrics. 1954;10(4):417–51.

    Article  Google Scholar 

  10. Cox DR. Regression models and life-tables. J R Stat Soc. 1972;34:187–220.

    Google Scholar 

  11. Dijkman B, Kooistra B, Bhandari M. How to work with a subgroup analysis. Can J Surg. 2009;52:515–22.

    PubMed  PubMed Central  Google Scholar 

  12. Dmitrienko A, Offen WW, Westfall PH. Gatekeeping strategies for clinical trials that do not require all primary effects to be significant. Stat Med. 2003;22:2387–400.

    Article  PubMed  Google Scholar 

  13. Dmitrienko A, Tamhane AC, Wang X, Chen X. Stepwise gatekeeping procedures in clinical trial applications. Biometrical J. 2006;48(6):984–91.

    Article  Google Scholar 

  14. Dmitrienko A, Tamhane AC. Gatekeeping procedures with clinical trial applications. Pharm Statistics. 2007;6:171–80.

    Article  Google Scholar 

  15. Dmitrienko A, Tamhane AC, Bretz F. Multiple testing problems in pharmaceutical statistics. 1st ed. Boca Raton: Chapman and Hall, CRC Biostatistics Series; 2009.

    Google Scholar 

  16. Dmitrienko A, D’Agostino RB. Tutorial in biostatistics: traditional multiplicity adjustment methods in clinical trials. Stat Med. 2013;32:5172–218.

    Article  PubMed  Google Scholar 

  17. Dunn OJ. Estimation of the medians for dependent variables. Ann Math Stat. 1959;30(1):192–7. doi:10.1214/aoms/1177706374.JSTOR2237135.

    Article  Google Scholar 

  18. Dunn OJ. Multiple comparisons among means. J Am Stat Assoc. 1961;56(293):52–64. doi:10.1080/01621459.1961.10482090.

    Article  Google Scholar 

  19. European Medicines Agency, Committee for Medicinal Products for Human Use. Concept paper on the need for a guideline on the use of subgroup analyses in randomized controlled trials. 2010. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2010/05/WC500090116.pdf. Accessed January 14, 2016.

  20. Freidlin B, Korn EL. Biomarker enrichment strategies: matching trial design to biomarker credentials. Nat Rev Clin Oncol. 2014;11(2):81–90.

    Article  PubMed  Google Scholar 

  21. Freidlin B, Korn EL, Gray R. Marker sequential test (MaST) design. Clin Trials. 2014;11(1):19–27.

    Article  PubMed  Google Scholar 

  22. Freidlin B, McShane LM, Korn EL. Randomized clinical trials with biomarkers: design issues. J Natl Cancer Inst. 2010;102:152.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Freidlin B, Simon R. Adaptive signature design: an adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clin Cancer Res. 2005;11:7872–78.

    Google Scholar 

  24. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–58.

    Article  PubMed  Google Scholar 

  25. Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika. 1988;75:800–2.

    Article  Google Scholar 

  26. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 1979;6(2):65–70.

    Google Scholar 

  27. Huedo-Medina, TB, Sánchez-Meca J, Marín-Martínez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistics or I2 index. Psychol Methods. 2006;11(2):193–206.

    Google Scholar 

  28. Lagakos SW. The challenge of subgroup analyses—reporting without distorting. N Engl J Med. 2006;354:1667–9.

    Article  CAS  PubMed  Google Scholar 

  29. Lee TH, Weisberg MC, Brand DA, Rouan GW, Goldman L. Candidates for thrombolysis among emergency room patients with acute chest pain. Ann Intern Med. 1989;110:957–62.

    Google Scholar 

  30. Mantel N, William Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22(4):719–48.

    CAS  PubMed  Google Scholar 

  31. Millen BA, Dmitrienko A. Chain procedures: a class of flexible closed testing procedures with clinical trial applications. Stat Biopharm Res. 2011;3:14–30.

    Article  Google Scholar 

  32. O’Brien PC. Procedures for comparing samples with multiple endpoints. Biometrics. 1984;40(4):1079–87.

    Article  PubMed  Google Scholar 

  33. Ondra T, Dmitrenko A, Friede T, Gra A, Miller F, Stallard N, Rosch M. Methods for identification and confirmation of targeted subgroups in clinical trials: a systematic review. J Biopharm Stat. 2016;26(1):99–119.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Oxman AD, Guyatt GH. A consumer’s guide to subgroup analyses. Ann Intern Med. 1992;116:78–84.

    Article  CAS  PubMed  Google Scholar 

  35. Pocock SJ, Hughes MD, Lee RJ. Statistical problems in the reporting of clinical trials. NEJM. 1987;317:426–32.

    Article  CAS  PubMed  Google Scholar 

  36. Pocock SJ, Assmann SF, Enos LE, Kasten LE. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med. 2002;21:2917–30.

    Article  PubMed  Google Scholar 

  37. Rosenblum M, Liu H, Yen E-H. Optimal tests of treatment effects for the overall population and two subpopulations in randomized trials, using sparse linear programming. J Am Stat Assoc. 2014;109(507):1216–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rothwell PM. Subgroup Analysis in randomized controlled trials: importance, indications, and interpretation. Lancet. 2005;2005(365):176–86.

    Article  Google Scholar 

  39. Schoenfeld D. Partial residuals for the proportional hazards regression model. Biometrika. 1982;69:239–41.

    Article  Google Scholar 

  40. Simon R. The use of genomics in clinical trial design. Clin Cancer Res. 2008;14:5984–93.

    Article  PubMed  Google Scholar 

  41. Simon R. Clinical trials for predictive medicine. Stat Med. 2012;31:3031–40.

    Article  PubMed  Google Scholar 

  42. Simon RM, Maitournam A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clin Cancer Res. 2004;10:6759–63.

    Article  CAS  PubMed  Google Scholar 

  43. Song Y, Chi GYH. A method for testing a prespecified subgroup in clinical trials. Stat Med. 2007;26:3535–49.

    Article  PubMed  Google Scholar 

  44. Sun X, Heels-Ansdell D, Walter SD, Guyatt G, Sprague S, Bhandari M, Sanders D, Schemitsch E, Tornetta P, Swiontkowski M. Is a subgroup claim believable? A user’s guide to subgroup analyses in the surgical literature. J Bone Joint Surg Am. 2011;93:e8.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Sun X, Briel M, Busse JW, You JJ, Akl EA, Mejza F, Bala MM, Bassler D, Mertz D, Diaz-Granados N, Vandvik PO, Malaga G, Srinathan SK, Dahm P, Johnston BC, Alonso-Coello P, Hassouneh B, Walter SD, Heels-Ansdell D, Bhatnagar N, Altman DG, Guyatt GH. Credibility of claims of subgroup effects in randomised controlled trials: systematic review. BMJ. 2012;344:e1553.

    Article  PubMed  Google Scholar 

  46. Sun X, Ioannidis JP, Agoritsas T, Alba AC, Guyatt G. How to use a subgroup analysis: users’ guide to the medical literature. JAMA. 2014;311:405–11.

    Article  CAS  PubMed  Google Scholar 

  47. Tanniou J, van der Tweel I, Teerenstra S, Roes KCB. Subgroup analysis in confirmatory clinical trials: time to be specific about their purposes. BMC Med Res Methodol. 2016;16:20.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Uno H, Claggett B, Tian L, Inoue E, Gallo P, Miyata T, Schrag D, Takeuchi M, Uyama Y, Zhao L, Skali H, Solomon S, Jacobus S, Hughes M, Packer M, Wei LJ. Moving beyond the hazard ratio in quantifying between-group difference in survival analysis. J Clin Oncol. 2014;32(22):2380–5.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wang R, Lagakos SW, Ware JH, Hunter DJ, Drazen JM. Statistics in medicine—reporting of subgroup analyses in clinical trials. NEJM. 2007;357(21):2189–94.

    Article  CAS  PubMed  Google Scholar 

  50. Westfall PH, Krishen A. Optimally weighted, fixed sequence and gatekeeper multiple testing procedures. J Stat Plan Infer. 2001;99:25–41.

    Article  Google Scholar 

  51. Wiens BL, Dmitrienko A. The fallback procedure for evaluating a single family of hypotheses. J Biopharm Stat. 2005;15(6):929–42.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cooperative Studies Program Coordinating Center, VA Palo Alto Health Care System, 701 North Shoreline Blvd, Mountain View, CA, USA

    Hui Wang, Ilana Belitskaya-Lévy, Mei-Chiung Shih & Ying Lu

  2. Department of Biomedical Data Science, Stanford University School of Medicine, 150 Governor’s Lane T101B, Stanford, CA, 94305-5405, USA

    Ying Lu

Authors
  1. Hui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilana Belitskaya-Lévy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mei-Chiung Shih
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Lu .

Editor information

Editors and Affiliations

  1. Department of Surgery, Boston VA Healthcare System Department of Surgery, West Roxbury, Massachusetts, USA

    Kamal M.F. Itani

  2. Depatment of Veteran Affairs, Cooperative Studies Program Coordinating Center, Hines VA Hospital, Hines, Illinois, USA

    Domenic J. Reda

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Wang, H., Belitskaya-Lévy, I., Shih, MC., Lu, Y. (2017). Advanced Statistical Methods. In: Itani, K., Reda, D. (eds) Clinical Trials Design in Operative and Non Operative Invasive Procedures. Springer, Cham. https://doi.org/10.1007/978-3-319-53877-8_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-53877-8_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53876-1

  • Online ISBN: 978-3-319-53877-8

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation