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Outwitting Procrustes

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Aaron M Tejani

on 18 July 2012

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Transcript of Outwitting Procrustes

Conflict Declaration Deliberate 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! skepticism Don't even bother Hierarchy of Outcomes Serious adverse events Clinical significance
vs statistical significance Surrogates vs tangible outcomes Issues to consider Example: LABA+ICS for COPD Death
Hospitalization (initial or prolonged)
Congenital anomaly
Intervention to prevent permanent damage Questions Randomization is magic Sequence generation The drug "did" it You need to check p-values small studies Allocation concealment Blinding 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 So what? Lack of:
allocation concealent Overestimation of effect size Surrogate Outcomes Outwitting Procrustes:
critical thinking and clinical trials Blood pressure Hemoglobin A1c LDL cholesterol Bone mineral density FEV 1 Scales and Scores MCID Linear?? Intention-to-treat 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 The Worst Last observation carried forward What you need to know How many people per group When did they leave? Significance and power Power Significance Absence of evidence
is not evidence of absence
of an effect Logical conclusions Examples p-value adjustments Poor man's Bonferroni 0.05 _____ questions/outcomes/subgroups = Adjusted p-value is it lower? set the
higher subgroup analyses effect within a subgroup Issue #1:
underpowered Issue #2:
false positives Issue #3:test for interaction Interaction test significant if
p <0.1, why? Issue #4: not random Problems unless pre-specified stress overall effect needs to be based on
pre-randomization characteristics plausible reason for difference
and predict direction of effect composite outcomes consider relative importance consider relative frequency counting rules no negative effects non-inferiority 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 levity declaration no mugs pens, coffee, honorariums, awards, etc... Need to do the accounting Maintains balance Keep the list secret false negatives false positives 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) Confidence Interval (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 objectives 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)
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