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Critical Appraisal: Epidemiology 101

Critical Appraisal: Epidemiology 101. POS Lecture Series April 28, 2004. What to Believe?. "A proof is a proof. What kind of a proof? It's a proof. A proof is a proof. And when you have a good proof, it's because it's proven.". Introduction. Why do I need Critical Appraisal Skills?

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Critical Appraisal: Epidemiology 101

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  1. Critical Appraisal:Epidemiology 101 POS Lecture Series April 28, 2004

  2. What to Believe?

  3. "A proof is a proof. What kind of a proof? It's a proof. A proof is a proof. And when you have a good proof, it's because it's proven."

  4. Introduction Why do I need Critical Appraisal Skills? • Not all literature accurate • Conclusions drawn not always possible • Why the inaccuracies? • Stupidity • “Publish or perish” • Money • Being cynical and suspicious is healthy

  5. The best defense is to be prepared

  6. Introduction • Types of studies • Important components of a good randomized trial • 6 important questions to ask yourself when reading a paper

  7. Study Types • Descriptive, Observational, Experimental • Descriptive – series, case report • Observational – groups determined by predetermined factor • Experimental – investigator in control of group assignments

  8. Types of StudiesObservational • Case-control • uses • Advantages and disadvantages • Cost, good for causation in rare disease • Recall bias

  9. Types of Studies Observational • Cohort • Definition • Advantages and disadvantages • Prospective • Cost high • Esp if disease is rare or time between exposure and onset of disease is long

  10. Types of StudiesExperimental • Randomized trial • “Gold Standard” • Advantages and disadvantages

  11. Principles of a Good Trial • Ideas, research question, hypothesis • Clinical relevance • Is it possible? • Time, finances, ethics

  12. Principles of a Good Trial • Literature search • Background • Results of other trials • Convinced it was extensive

  13. Principles of a Good Trial • Patient Selection • Inclusion and exclusion criteria • Are they well defined? • Are they reasonable? • Are they clinically relevant? • Do they change the results?

  14. Principles of a Good Trial • Sample size calculation • Most ortho literature does not mention • There is SOME science • Based on primary outcome measurement

  15. Sample Size Calculation • n = 2 [( + ) / ] 2 • Z of α (Type one error) • Usually 0.05 z=1.96 • Z of β (Type II error) • Usually 0.2 Z=1.28

  16. Sample Size Calculation • n = 2 [( + ) / ] 2 •  = S.D. of outcome measure • How do you know?? • Pilot study • published

  17. Sample Size Calculation • n = 2 [( + ) / ] 2 •  = Clinically relevant difference • This is the variable that can be manipulated • Depends of risks/cost of treatment

  18. Sample Size Calculation • n = 2 [( + ) / ] 2 • Equivalency trial • Rarely done =0.05 and sample size increases • A neg trial that does not address this can not conclude “no difference in treatments” only “we failed to prove a difference”

  19. Randomization • Computer, random number table, coin toss • Not birthday, MCP • Block randomization • Small number, multi-center • AABB, ABBA, etc • Potential for bias

  20. Blinding • Always adds weight to a study • Are the subject and investigators blinded • Is it feasable or possible?

  21. Intervention • Well defined, particulars discussed

  22. Outcome Measurement • Primary outcome measure • Secondary outcome measures • Data dredging

  23. Analysis • Biostats • Definitely some trust here • Everyone can’t be an expert

  24. Relative Risk Reduction(RRR) RRR = (0.1 – 0.05)/ 0.1 = 50% If outcome is rare, this is misleading

  25. Absolute Risk Reduction(ARR) ARR = 0.1 – 0.05 = 5% Good for rare outcomes and NNT

  26. Number Needed to Treat(NNT) ARR = 0.1 – 0.05 = 5% NNT = 1/ARR = 1/0.05 = 20

  27. Lost to Follow-up • 20 % added to sample size • Good Investigators very aggressive • “Worse case” Analysis

  28. Six Questions to Ask before you change your practice!

  29. 1. Really Randomized?

  30. 2. All clinically relevant outcomes Reported?

  31. 3. Patients similar to your own?

  32. 4. Was clinical and statistical significant considered?

  33. 5. Is the intervention feasible in your practice?

  34. 6. All patients accounted for?

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