Systematic Reviews and Meta-Analyses of Oncology Studies

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Abstract

This chapter is intended principally for practicing clinicians who want to understand the concepts of and reasons for conducting systematic reviews and meta-analyses in oncology. Although there are a few striking examples of cancer treatments that really do work extremely well, most claims for efficacy turn out to be limited. Uncertainties coming from results obtained by different clinical studies need to be interpreted. Systematic reviews can define whether scientific findings are consistent and can be generalized across populations and treatment variations, or whether findings vary significantly by particular subsets. Meta-analyses can increase the power and precision of estimates of treatment effects and exposure risks. Explicit methods should be used to limit bias and improve the reliability and accuracy of conclusions. In the field of clinical oncology, there are several reasons for conducting a systematic review with meta-analyses. Here we discuss how to perform and interpret these studies, and present the main statistical concepts with examples from the literature.

Keywords

Systematic review Meta-analysis Oncology Cancer Individual patient data Metabias 
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References

  1. 1.
    Mulrow CD. Rationale for systematic reviews. BMJ. 1994;309(6954):597–9.CrossRefGoogle Scholar
  2. 2.
    Tebala GD. What is the future of biomedical research? Med Hypotheses. 2015;85(4):488–90.CrossRefGoogle Scholar
  3. 3.
    Pereira AA, Rego JF, Munhoz RR, Hoff PM, Sasse AD, Riechelmann RP. The impact of complete chemotherapy stop on the overall survival of patients with advanced colorectal cancer in first-line setting: a meta-analysis of randomized trials. Acta Oncol. 2015;54(10):1737–46.CrossRefGoogle Scholar
  4. 4.
    Sampson M, Barrowman NJ, Moher D, Klassen TP, Pham B, Platt R, et al. Should meta-analysts search EMBASE in addition to Medline? J Clin Epidemiol. 2003;56(10):943–55.CrossRefGoogle Scholar
  5. 5.
    Sasse AD, Santos L. Searching LILACS database is irrelevant in systematic reviews in oncology. In: Evidence in the era of globalisation. Abstracts of the 16th Cochrane colloquium. Freiburg, Germany. p. 2008.Google Scholar
  6. 6.
    Chan AW, Hrobjartsson A, Haahr MT, Gotzsche PC, Altman DG. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA. 2004;291(20):2457–65.CrossRefGoogle Scholar
  7. 7.
    Lee K, Bacchetti P, Sim I. Publication of clinical trials supporting successful new drug applications: a literature analysis. PLoS Med. 2008;5(9):e191.CrossRefGoogle Scholar
  8. 8.
    Dwan K, Altman DG, Arnaiz JA, Bloom J, Chan AW, Cronin E, et al. Systematic review of the empirical evidence of study publication bias and outcome reporting bias. PLoS One. 2008;3(8):e3081.CrossRefGoogle Scholar
  9. 9.
    Kicinski M. Publication bias in recent meta-analyses. PLoS One. 2013;8(11):e81823.CrossRefGoogle Scholar
  10. 10.
    Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34.CrossRefGoogle Scholar
  11. 11.
    Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088–101.CrossRefGoogle Scholar
  12. 12.
    Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455–63.CrossRefGoogle Scholar
  13. 13.
    Terrin N, Schmid CH, Lau J. In an empirical evaluation of the funnel plot, researchers could not visually identify publication bias. J Clin Epidemiol. 2005;58(9):894–901.CrossRefGoogle Scholar
  14. 14.
    Sterne JA, Sutton AJ, Ioannidis JP, Terrin N, Jones DR, Lau J, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002.CrossRefGoogle Scholar
  15. 15.
    Hopewell S, McDonald S, Clarke M, Egger M. Grey literature in meta-analyses of randomized trials of health care interventions. Cochrane Database Syst Rev. 2007;2. MR000010Google Scholar
  16. 16.
    McAuley L, Pham B, Tugwell P, Moher D. Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses? Lancet. 2000;356(9237):1228–31.CrossRefGoogle Scholar
  17. 17.
    Fergusson D, Laupacis A, Salmi LR, McAlister FA, Huet C. What should be included in meta-analyses? An exploration of methodological issues using the ISPOT meta-analyses. Int J Technol Assess Health Care. 2000;16(4):1109–19.CrossRefGoogle Scholar
  18. 18.
    Burdett S, Stewart LA, Tierney JF. Publication bias and meta-analyses: a practical example. Int J Technol Assess Health Care. 2003;19(1):129–34.CrossRefGoogle Scholar
  19. 19.
    Cook DJ, Guyatt GH, Ryan G, Clifton J, Buckingham L, Willan A, et al. Should unpublished data be included in meta-analyses? Current convictions and controversies. JAMA. 1993;269(21):2749–53.CrossRefGoogle Scholar
  20. 20.
    Tetzlaff J, Moher D, Pham B, Altman D, editors. Survey of views on including grey literature in systematic reviews. 14th Cochrane Colloquium; Dublin, Ireland. 2006.Google Scholar
  21. 21.
    Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700.CrossRefGoogle Scholar
  22. 22.
    Moher D, Liberati A, Tetzlaff J, Altman DG, Prisma Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009;62(10):1006–12.CrossRefGoogle Scholar
  23. 23.
    Shea BJ, Grimshaw JM, Wells GA, Boers M, Andersson N, Hamel C, et al. Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol. 2007;7:10.CrossRefGoogle Scholar
  24. 24.
    Higgins JPT, Green S (editors). Cochrane handbook for systematic reviews of interventions version 5.1.0 [updated March 2011]. The cochrane collaboration, 2011. Available from http://handbook.cochrane.org.
  25. 25.
    Buscemi N, Hartling L, Vandermeer B, Tjosvold L, Klassen TP. Single data extraction generated more errors than double data extraction in systematic reviews. J Clin Epidemiol. 2006;59(7):697–703.CrossRefGoogle Scholar
  26. 26.
    Keene ON. Alternatives to the hazard ratio in summarizing efficacy in time-to-event studies: an example from influenza trials. Stat Med. 2002;21(23):3687–700.CrossRefGoogle Scholar
  27. 27.
    Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8:16.CrossRefGoogle Scholar
  28. 28.
    Moher D, Pham B, Jones A, Cook DJ, Jadad AR, Moher M, et al. Does quality of reports of randomised trials affect estimates of intervention efficacy reported in meta-analyses? Lancet. 1998;352(9128):609–13.CrossRefGoogle Scholar
  29. 29.
    Higgins JP, Altman DG, Gotzsche PC, Juni P, Moher D, Oxman AD, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ. 2011;343:d5928.CrossRefGoogle Scholar
  30. 30.
    Jadad AR, Moore RA, Carroll D, Jenkinson C, Reynolds DJ, Gavaghan DJ, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials. 1996;17(1):1–12.CrossRefGoogle Scholar
  31. 31.
    Verhagen AP, de Vet HC, de Bie RA, Kessels AG, Boers M, Bouter LM, et al. The Delphi list: a criteria list for quality assessment of randomized clinical trials for conducting systematic reviews developed by Delphi consensus. J Clin Epidemiol. 1998;51(12):1235–41.CrossRefGoogle Scholar
  32. 32.
    Pildal J, Hrobjartsson A, Jorgensen KJ, Hilden J, Altman DG, Gotzsche PC. Impact of allocation concealment on conclusions drawn from meta-analyses of randomized trials. Int J Epidemiol. 2007;36(4):847–57.CrossRefGoogle Scholar
  33. 33.
    Wood L, Egger M, Gluud LL, Schulz KF, Juni P, Altman DG, et al. Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study. BMJ. 2008;336(7644):601–5.CrossRefGoogle Scholar
  34. 34.
    Holmes J, Herrmann D, Koller C, Khan S, Umberham B, Worley JA, et al. Heterogeneity of systematic reviews in oncology. Proc (Bayl Univ Med Cent). 2017;30(2):163–6.CrossRefGoogle Scholar
  35. 35.
    Borenstein M, Hedges LV, Higgins JP, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010;1(2):97–111.CrossRefGoogle Scholar
  36. 36.
    Higgins J, Thompson S, Deeks J, Altman D. Statistical heterogeneity in systematic reviews of clinical trials: a critical appraisal of guidelines and practice. J Health Serv Res Policy. 2002;7(1):51–61.CrossRefGoogle Scholar
  37. 37.
    Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.CrossRefGoogle Scholar
  38. 38.
    Munhoz RR, Pereira AA, Sasse AD, Hoff PM, Traina TA, Hudis CA, et al. Gonadotropin-releasing hormone agonists for ovarian function preservation in premenopausal women undergoing chemotherapy for early-stage breast cancer: a systematic review and meta-analysis. JAMA Oncol. 2016;2(1):65–73.CrossRefGoogle Scholar
  39. 39.
    Stewart LA, Tierney JF. To IPD or not to IPD? Advantages and disadvantages of systematic reviews using individual patient data. Eval Health Prof. 2002;25(1):76–97.CrossRefGoogle Scholar
  40. 40.
    Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924–6.CrossRefGoogle Scholar
  41. 41.
    Hultcrantz M, Rind D, Akl EA, Treweek S, Mustafa RA, Iorio A, et al. The GRADE Working Group clarifies the construct of certainty of evidence. J Clin Epidemiol. 2017;87:4–13.CrossRefGoogle Scholar
  42. 42.
    Guyatt GH, Haynes RB, Jaeschke RZ, Cook DJ, Green L, Naylor CD, et al. Users' guides to the medical literature: XXV. Evidence-based medicine: principles for applying the users' guides to patient care. Evidence-Based Medicine Working Group. JAMA. 2000;284(10):1290–6.CrossRefGoogle Scholar
  43. 43.
    Villar J, Carroli G, Belizan JM. Predictive ability of meta-analyses of randomised controlled trials. Lancet. 1995;345(8952):772–6.CrossRefGoogle Scholar
  44. 44.
    Cappelleri JC, Ioannidis JP, Schmid CH, de Ferranti SD, Aubert M, Chalmers TC, et al. Large trials vs meta-analysis of smaller trials: how do their results compare? JAMA. 1996;276(16):1332–8.CrossRefGoogle Scholar
  45. 45.
    LeLorier J, Gregoire G, Benhaddad A, Lapierre J, Derderian F. Discrepancies between meta-analyses and subsequent large randomized, controlled trials. N Engl J Med. 1997;337(8):536–42.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Gastrointestinal Medical OncologyUniversity of Texas – M.D. Anderson Cancer CenterHoustonUSA
  2. 2.Department of Internal Medicine, Faculty of Medical SciencesUniversity of Campinas (UNICAMP)CampinasBrazil

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