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 Studies Observational 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 Studies Experimental 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) UnreamedReamed Non-Union Rate.1.05 RRR = (0.1 – 0.05)/ 0.1 = 50% If outcome is rare, this is misleading

25. Absolute Risk Reduction (ARR) UnreamedReamed Non-Union Rate.1.05 ARR = 0.1 – 0.05 = 5% Good for rare outcomes and NNT

26. Number Needed to Treat (NNT) UnreamedReamed Non-Union Rate.1.05 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?