evidence-based medicine: ebm 101 - duke university · the evidence-based medicine cycle: everything...
TRANSCRIPT
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TherapyApril 7th, 2015
Manny Ribeiro, MD Larry Young, MD
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Randomized Controlled Trial: the smoking hot evidence
Manny Ribeiro, MD Larry Young, MD
April 7th, 2015
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Smoking the evidence until the end
Manny Ribeiro, MD Larry Young, MD
April 7th, 2015
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Objectives
The evidence-based medicine cycle Start with a case scenario Ask the clinical question Critical appraisal
• Allocation concealment• Intention-to-treat• Blinding• Follow-up
Results: making the math simple!
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THE PATIENT
ASSESS
ASK
ACQUIRE
APPLY
APPRAISE
Evidence-based
Medicine Cycle
The 5 A’s
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Case scenario
A 67-year-old man presented with productive cough and fever for 3 days
Past medical history of coronary artery disease, with a myocardial infarction two years prior
Heavy smoker, 2 packs of cigarettes per day for 50 years (100 pack-year)
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Bad pneumonia!
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Also had a new myocardial infarction
• Echocardiogram with a drop in ejection fraction to 46% and new segmental wall motion abnormality
Case scenario
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His daughter’s request
“Please start something for my father to quit smoking before he
goes home, otherwise he will just go back to it right after discharge.”
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This sounds like PICOTT...
Can we PICOTT
this?
Yes, this is “PICOTT”able!
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P
I
C
O
T
T
Clinical question formation
opulation
ntervention
omparison
utcome
ype of Question
ype of (ideal) study design
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P
I
C
O
T
T
Clinical question
Smokers admitted to the hospital
Inpatient strategies for smoking cessation
No/other strategies
Quit rate, pneumonia, myocardial infarction
Therapy question
Randomized controlled trial
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Search strategy
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Critical appraisal= Randomized
Controlled Trial
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Goals of randomization
P R
Treatment
Control
Prognosis X
Prognosis X
To keep all known and unknown prognostic variables evenly distributed between the groups
Outcome A
OutcomeB
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Group C
Group D
Group A
Group B
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Allocation concealment
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Our population
YOU!
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List generation
Head: Allowed to ask questions (A)
Tail: NOT Allowed to ask questions (NA)
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Allocation concealment
P OR
Listgeneration
Allocationconcealment
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Allocation concealment
The person who is enrolling participants cannot know, predict, or manipulate the list
Trials with inappropriate allocation concealment are associated with larger estimates of treatment effect
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Intention-to-treat
Teaching method 1
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Dead
Alive
Alive
Dead
Dead
Alive
Dead
Alive
R
Treatment 1
Treatment 2
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Dead
Alive
Alive
Dead
Dead
Alive
Dead
Alive
R
Treatment 1
Treatment 2
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Dead
Alive
Alive
Dead
Dead
Alive
Dead
Alive
Dead
Alive
Alive
Dead
Dead
Alive
Dead
Alive
R
Treatment 1
Treatment 2
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Intention-to-treat
Cerebro-
vascular
disease
R
Surgery+ ASA
ASA200
100
100
Stroke
Stroke
10
10
Stroke
Stroke
10
10
Surgery
PerProtocol
10/90 =11%
20/100 =20%
ITT
20/100 =20%
20/100 =20%
RD = 9%
RD = 0%
Teaching method 2
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Intention-to-treat… Why??
Preserves balance between the groups
Reflects real life
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Blinding
Patient CaregiverCollector
of outcomedata
Adjudicatorof outcome
Data analyst
5 volunteers!
The authors should tell us who was blinded!
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Blinding
P OR
Listgeneration
Allocationconcealment
Blinding
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Blinding
Trials with inappropriate blinding are also associated with larger estimates of effect, but not as much as with inappropriate allocation concealment
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Allocation Concealment
Blinding
Who? Enroller
Patients, caregivers, data
collectors, adjudicators,
analysts
What? The listGroup
assignments
When?Part of
randomizationAfter
randomization
Allocation Concealment x blinding
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Follow-up
Patients who are lost often have different prognoses from those who are retained
Strategies to deal with lost to follow up: last observation carried forward, worst-case scenario
The best solution is to assure a good follow up
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Therapy Math
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All-cause hospital admissions
Step 2: Subtract:
Step 3: Divide:
Step 1:
Intensive Usual
23%
41%
41% – 23% = 18%
23% / 41% = 0.56
Riskdifference
Risk ratio
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Number Needed to Treat
What is the risk difference of 18% telling you?
In order to save 18, you needed to treat 100
In order to save 1, how many do you need to treat?
FormulaNNT: 100 / RD
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Number Needed to Treat
Formula: NNT = 100 / RDNNT = 100 / 18 = 5.5NNT = 6
You needed to treat 6 patients in order to prevent one extra hospitalization in 2 years
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Relative Risk Reduction
Teaching method 1
20
By how much (in %) did I reduce?
Answer: 25%
15
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All-cause hospital admissions
Step 2: Subtract:
Step 3: Divide:
Step 1:
Intensive Usual
23%
41%
41% – 23% = 18%
23% / 41% = 0.56
Riskdifference
Risk ratio
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Relative Risk Reduction
Teaching method 2 Formula: RRR = 1 - RR
Intensive Usual
23%
41%
23% / 41% = 0.56 Risk ratio
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Relative Risk Reduction
Teaching method 2 Formula: RRR = 1 - RR
Intensive Usual
23%
41%
23% / 41% = 0.56 Risk ratio
41%
41% / 41% = 1
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Relative Risk Reduction
Teaching method 2 Formula: RRR = 1 - RR
Intensive Usual
23%
41%
23% / 41% = 0.56 Risk ratio
1
RRR = 0.44 (44%)
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Math time!
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Mortality
Intensive Usual
2.8%
12%
Risk difference: 9.2%Risk Ratio: 0.23
NNT: 11RRR: 0.77 (77%)
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Take-home points
The evidence-based medicine cycle: everything starts and ends with a patient Improper allocation concealment can overestimate the effect size For therapy papers, ITT is preferred to per-protocol analysis Look for who was blinded in the study Make the math simple! Remember to subtract and divide!
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Teaching Take-home Points
• What strategies did we use to teach these concepts?
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Teaching Take-home Points
Real clinical case – start with a patient. Clinical question related to different specialties, and even non-medical learners Group activities Pre-mark article – saves time; reduces stress Imperfect articles (you can teach with them) Interactivity Different teaching strategies (visual learners, math lovers) Simplicity: one step at a time, with “no man left behind”
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Back to our patient…
Two months after discharge:
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Thank you!