DOI QR코드

DOI QR Code

Importance of Meta-Analysis and Practical Obstacles in Oncological and Epidemiological Studies: Statistics Very Close but Also Far!

  • Tanriverdi, Ozgur (Department of Medical Oncology, Faculty of Medicine, Mugla Sitki Kocman University) ;
  • Yeniceri, Nese (Department of Family Medicine, Faculty of Medicine, Mugla Sitki Kocman University)
  • Published : 2015.03.04

Abstract

Studies of epidemiological and prognostic factors are very important for oncology practice. There is a rapidly increasing amount of research and resultant knowledge in the scientific literature. This means that health professionals have major challenges in accessing relevant information and they increasingly require best available evidence to make their clinical decisions. Meta-analyses of prognostic and other epidemiological factors are very practical statistical approaches to define clinically important parameters. However, they also feature many obstacles in terms of data collection, standardization of results from multiple centers, bias, and commentary for intepretation. In this paper, the obstacles of meta-analysis are briefly reviewed, and potential problems with this statistical method are discussed.

Keywords

Meta-analysis;obstacles;oncology;cancer research

References

  1. Akobeng AK (2005). Understanding systematic reviews and meta-analysis. Arch Dis Child, 90, 845-8. https://doi.org/10.1136/adc.2004.058230
  2. Bax L, Moons KG (2011). Beyon publication bias. J Clin Epidemiol, 64, 459-462 https://doi.org/10.1016/j.jclinepi.2010.09.003
  3. Berman NG, Parker RA (2002). Meta-analysis: Neither quick nor easy. BMC Med Res Methodol, 2, 10. https://doi.org/10.1186/1471-2288-2-10
  4. Burgersm JS, Fervers B, Haugh M, et al (2004). International assessment of the quality of clinical practice guidelines in oncology using the appraisal of guidelines and research and evaluation instrument. J Clin Oncol, 22, 2000-7. https://doi.org/10.1200/JCO.2004.06.157
  5. Columb MO, Lalkhen AG (2005). Systematic reviews and meta-analyses. Current Anesthesia Critical Care, 16, 391-4. https://doi.org/10.1016/j.cacc.2006.02.004
  6. Early Breast Cancer Trialists' Collaborative Group (1998). Tamoxifen for early breast cancer: an overview of the randomised trials. Lancet, 35, 1451-67.
  7. Engberg S (2008). Systematic reviews and meta-analysis. J Wound Ostomy Continence Nurs, 35, 258-265. https://doi.org/10.1097/01.WON.0000319122.76112.23
  8. Gonzalez IF, Urrutia G, Alonso-Coello P (2011). Systematic Reviews and Meta-Analysis: Scientific rationale and interpretation. Rev Esp Cardiol, 64, 688-96. https://doi.org/10.1016/j.recesp.2011.03.029
  9. Higgins JP, Thompson SG, Deeks JJ, Altman D (2003). Measuring inconsistency in meta-analyses. BMJ, 327, 557-560. https://doi.org/10.1136/bmj.327.7414.557
  10. Impellizzeri FM, Bizzini M (2012). Systematic review and meta-analysis: a primer. Int J Sports Phys Ther, 7, 493-503.
  11. Ioannidis JP (2005). Why most published research findings are false. PLos Med, 2, 124. https://doi.org/10.1371/journal.pmed.0020124
  12. Ioannidis JP (2008). Interpretation of tests of heterogeneity and bias in meta-analysis. J Eval Clin Pract, 14, 951-7. https://doi.org/10.1111/j.1365-2753.2008.00986.x
  13. Khan HFR, Saxena A, Gabbidon K, Ross E, Shrestha A (2014a). Statistical applications for the prediction of white Hispanic breast cancer survival. Asian Pac J Cancer Prev, 15, 5571-5. https://doi.org/10.7314/APJCP.2014.15.14.5571
  14. Khan HFR, Saxena A, Rana S, Ahmed NU (2014b). Bayesian method for modeling male breast cancer survival data. Asian Pac J Cancer Prev, 15, 663-9. https://doi.org/10.7314/APJCP.2014.15.2.663
  15. Kranke P (2010). Evidence-based practice: how to perform and use systematic reviews for clinical decision-making. Eur J Anaesthesiol, 27, 763-72. https://doi.org/10.1097/EJA.0b013e32833a560a
  16. Lyman GH, Kuderer NM (2005). The strengths and limitations ofmeta-analyses based on aggregate data. BMC Med Res Methodol, 5, 14. https://doi.org/10.1186/1471-2288-5-14
  17. Noble JH (2006). Meta-analysis: Methods, strengths, weaknesses and political uses. J Lab Clin Med, 147, 7-20. https://doi.org/10.1016/j.lab.2005.08.006
  18. Noordzij M, Hooft L, Dekker FW, Zoccali J, Jager K.J (2009). Systematic reviews and meta-analyses: when they are useful and when to be careful. Kidney Int, 76, 1130-6. https://doi.org/10.1038/ki.2009.339
  19. Pace NL, Stat M (2011). Research methods for meta-analyses. Best Practice and Res Clinical Anesthesiol, 25, 523-33. https://doi.org/10.1016/j.bpa.2011.08.005
  20. Peters JL, Sutton AJ, Jones DK, Abrams KR, Rushton R (2006) Comparison of two methods to detect publication bias in meta-analysis, JAMA, 295, 670-680.
  21. Sackett DL, Srs SE, Richardson WS, Rosenberg W, Haynes RB. Evidence-Based Medicine: How to Practice and Teach EBM, 2nd edn. 2000, Edinburgh: Churchill Livingstone.
  22. Saveleva E, Selinski S (2008). Meta-analyses with Binary Outcomes: how many studies need to be omitted to detect a publication bias? J Toxicol Environ Healt, 71, 845-50. https://doi.org/10.1080/15287390801985844
  23. Saxena U, Sauvagelt C, Sankaranarayanan R (2012). Evidencebased screening, early diagnosis and treatment strategy of cervical cancer for National Policy in low-resource countries:example of India. Asian Pac J Cancer Prev, 13, 1699-1703. https://doi.org/10.7314/APJCP.2012.13.4.1699
  24. Thornton A, Lee P (2000). Publication bias in meta-analysis: its causes and consequences. J Clin Epidemiol, 53, 207-216. https://doi.org/10.1016/S0895-4356(99)00161-4
  25. Tilburt JC (2008). Evidence-based medicine beyond the bedside:keeping an eye on context. J Eval Clin Pract, 14, 721-5. https://doi.org/10.1111/j.1365-2753.2008.00948.x
  26. Tricco AC, Tetzlaff J, Moher D(2011). The art and science of knowledge synthesis. J Clin Epidemiol, 64, 11-20. https://doi.org/10.1016/j.jclinepi.2009.11.007
  27. van Enst MA, Ochodo E, Scholten R, Hooft L (2014). Investigation of publication bias in meta-analyses of diagnostic test accuracy: a meta-epidemiological study. BMC Med Res Methodol, 14, 70. https://doi.org/10.1186/1471-2288-14-70
  28. Vigna-Taglianti F, Vineis P, Liberati A, Faggiano f(2006). Quality of Systematic Reviews used in guidelines for oncology practice. Ann Oncol, 17, 691-701. https://doi.org/10.1093/annonc/mdl003
  29. Wu YZ, Yang H, Zhang L, et al (2012). Application of crossover analysis-logistic regression in the assessment of geneenviromental interactions for colorectal cancer. Asian Pac J Cancer, 13, 2031-7. https://doi.org/10.7314/APJCP.2012.13.5.2031
  30. Zwahlen M, Renehan A, Egger M (2008). Meta-analysis in medical research: Potentials and limitations. Urol Oncol, 26, 320-9. https://doi.org/10.1016/j.urolonc.2006.12.001