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Transcript of Outwitting Procrustes
Aaron M Tejani
Lower mainland pharmacy services
Therapeutics Initiative (UBC)
Cochrane hypertension review group
Who was Procrustes?
Quick read of trials
Come up with your own question!
Don't even bother
Hierarchy of Outcomes
Serious adverse events
vs statistical significance
Surrogates vs tangible outcomes
Issues to consider
Example: LABA+ICS for COPD
Hospitalization (initial or prolonged)
Intervention to prevent permanent damage
Randomization is magic
The drug "did" it
You need to check
All outcomes can be influenced
(especially subjective outcomes)
Clinical effects may compromise blinding
Lack of blinding
could bias assessment
could bias actual outcome
Critical appraisal leads to different conclusions
Overestimation of effect size
critical thinking and clinical trials
Bone mineral density
Scales and Scores
Beyond numerators and denominators
20% overestimation of effect
19% of findings become non-significant
Loss to follow up
What you need to know
Report numbers lost in each group
Problem if loss >20% of randomized population
Consider worst case scenario
Last observation carried forward
What you need to know
How many people per group
When did they leave?
Significance and power
Absence of evidence
is not evidence of absence
of an effect
Poor man's Bonferroni
is it lower?
effect within a subgroup
Issue #3:test for interaction
Interaction test significant if
p <0.1, why?
stress overall effect
needs to be based on
plausible reason for difference
and predict direction of effect
consider relative importance
consider relative frequency
no negative effects
sample size issues
margin is critical
What to look at
where does the margin come from?
is it justified?
is it less than a clinically important difference?
look at "worst" end of confidence interval
look at per protocol
i.e enhance differences
no mugs pens, coffee, honorariums, awards, etc...
Need to do the accounting
Keep the list secret
p values, NNT, ARR/ARI
p-value = probability of chance
relative risk= risk with Tx / risk with control
(1 = same risk or no difference)
absolute risk increase/reduction = risk with Tx -risk with control
(0 = same risk or no difference)
relative risk reduction
5-10/10 = 0.50
(says nothing about magnitude)
(if repeated, the true parameter would be within this range 95% of the time)
aaron m tejani, vijaya musini, ken bassett, tom perry
no conflicts to declare
list the sources of bias within a randomized controlled trial
identify the importance of methodological characteristics to help reduce bias (e.g. blinding)
outline steps required to interpret different analyses within and RCT (e.g. subgroups, intention to treat, composite outcomes)
describe the implications of different types of bias on the interpretation of data from RCTs
describe what p-values, confidence intervals, and effect estimates mean (odds ratio, absolure risk reduction)