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

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What to Believe?

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"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."

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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

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The best defense is to be prepared

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

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

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Types of Studies Observational Case-control –uses –Advantages and disadvantages Cost, good for causation in rare disease Recall bias

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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

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Types of Studies Experimental Randomized trial “Gold Standard” –Advantages and disadvantages

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

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Principles of a Good Trial Literature search –Background –Results of other trials –Convinced it was extensive

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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?

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

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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

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Sample Size Calculation n = 2 [( + ) / ] 2 = S.D. of outcome measure –How do you know?? Pilot study published

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Sample Size Calculation n = 2 [( + ) / ] 2 = Clinically relevant difference –This is the variable that can be manipulated –Depends of risks/cost of treatment

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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”

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

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

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Intervention Well defined, particulars discussed

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Outcome Measurement Primary outcome measure Secondary outcome measures –Data dredging

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Analysis Biostats –Definitely some trust here –Everyone can’t be an expert

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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

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

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Number Needed to Treat (NNT) UnreamedReamed Non-Union Rate.1.05 ARR = 0.1 – 0.05 = 5% NNT = 1/ARR = 1/0.05 = 20

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

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Six Questions to Ask before you change your practice!

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1. Really Randomized?

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2. All clinically relevant outcomes Reported?

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3. Patients similar to your own?

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4. Was clinical and statistical significant considered?

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5. Is the intervention feasible in your practice?

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6. All patients accounted for?

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