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? –Not all literature accurate –Conclusions drawn not always possible –Why the inaccuracies? Stupidity “Publish or perish” Money –Being cynical and suspicious is healthy

The best defense is to be prepared

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

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

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

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

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

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

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

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?

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

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

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

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

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”

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

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

Intervention Well defined, particulars discussed

Outcome Measurement Primary outcome measure Secondary outcome measures –Data dredging

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

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

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

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

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

Six Questions to Ask before you change your practice!

1. Really Randomized?

2. All clinically relevant outcomes Reported?

3. Patients similar to your own?

4. Was clinical and statistical significant considered?

5. Is the intervention feasible in your practice?

6. All patients accounted for?