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Bastian ohri-2015


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Slides for my talk at the 10th annual Clinical Research Training Course, OHRI, Ottawa on 22 October 2015

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Bastian ohri-2015

  1. 1. Hilda Bastian OHRI 10th Annual Clinical Research Training Course, Ottawa 22 October 2015 @hildabast “EBM” 2015: Thinking About Equity, Applicability, & Efficiency in Health Research
  2. 2. Views expressed in this presentation are personal and do not necessarily reflect the views of: • National Center for Biotechnology Information (NCBI) • National Library of Medicine (NLM) • National Institutes of Health (NIH) • US Department of Health and Human Services
  3. 3. Author & sponsor biases Financial Intellectual Ideological
  4. 4. Diversity: rights & impact In researchers In public involvement In clinical care * Bastian H. Speaking up for ourselves: the evolution of consumer advocacy in health care. Int J Technol Assessment Health Care 1998; 14: 3-23.
  5. 5. Effective interventions for bias & stigma? Cooper LA et al. Patient-centered communication, ratings of care and concordance of patient and physician race. Ann Intern Med 2003; 139: 907-915. New sources of stigma emerge, too
  6. 6. Expanding trial populations Women Children Elderly
  7. 7. Forms of methods abuse Too much faith in their power Reducing quality when they become ubiquitous
  8. 8. 1979: 14 trials a day • Cochrane Database Systematic Reviews by the numbers ( • Moher et al (2007). Epidemiology & reporting characteristics of systematic reviews. PLOS Medicine. • 2014 update of Moher (2007) by Page et al, Cochrane Vienna ID RO 4.6 Data updated from 2007 to 2013
  9. 9. 130 trials a day
  10. 10. 52 systematic reviews a day
  11. 11. Mismatch of methods with problems increasing inequity? Communication & knowledge translation research Unrepresentative study populations Situations that are too far from reality Need more qualitative research & realistic evaluation mechanisms Lorenc T et al. What types of intervention generate inequalities? Evidence from systematic reviews. J Epidemiol Community Health 2013; 67: 190-193. Bastian H. Just how demanding can we get before we blow it? BMJ 2003; 326: 1277-1278.
  12. 12. Unrepresentative study populations, highly artificial contexts My own data extraction for a single meta-analysis in: Stacey D et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev 2011, Issue 10.
  13. 13. Stacey D et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev 2011, Issue 10.
  14. 14. Example: the NNT Widely advocated without good evidence for its claims Hard to understand (11 trials, 26,879 people*) Bias towards under- estimating benefit/harm? * Bastian H. Mind your “p”s, RRs & NNTs… Absolutely Maybe March 2015: Bastian H. The NNT: An overhyped & confusing statistic. Third Opinion (MedPage Today) March 2015:
  15. 15. Some solutions Better standardized outcomes (e.g. OMERACT) Reviews of qualitative research informing other research (Good) patient public involvement
  16. 16. The consumer/patient journey as a guide
  17. 17. Minimizing bias in research dissemination & knowledge translation Increasing critical health literacy
  18. 18. • More support needed for methodology research • Better retrievability & linkages of trials & systematic reviews • Language barrier in systematic reviewing
  19. 19. Optimize peer-to-peer use of systematic reviews “Don’t tell me what to do – Show me how to do it.” Ways of using machines >> less time tedious effort, more time thinking Glasziou P et al. Taking healthcare interventions from trial to practice. BMJ 2010, 341.
  20. 20. We need machine-friendly researchers & agencies • True open access is also for data mining & re-use • Reporting requirements could be more specific
  21. 21. ICMJE: - trial registration number at the end of the abstract - Authors always list the number in citation & when first mentioned CONSORT: Bastian H. Why aren’t we all machine-friendly researchers? Absolutely Maybe October 2015.
  22. 22. Thank you!