evidence-based medicine: ebm 101 - duke university · the evidence-based medicine cycle: everything...
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TherapyApril 7th, 2015
Manny Ribeiro, MD Larry Young, MD
Randomized Controlled Trial: the smoking hot evidence
Manny Ribeiro, MD Larry Young, MD
April 7th, 2015
Smoking the evidence until the end
Manny Ribeiro, MD Larry Young, MD
April 7th, 2015
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!
THE PATIENT
ASSESS
ASK
ACQUIRE
APPLY
APPRAISE
Evidence-based
Medicine Cycle
The 5 A’s
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)
Bad pneumonia!
Also had a new myocardial infarction
• Echocardiogram with a drop in ejection fraction to 46% and new segmental wall motion abnormality
Case scenario
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.”
This sounds like PICOTT...
Can we PICOTT
this?
Yes, this is “PICOTT”able!
P
I
C
O
T
T
Clinical question formation
opulation
ntervention
omparison
utcome
ype of Question
ype of (ideal) study design
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
Search strategy
Critical appraisal= Randomized
Controlled Trial
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
Group C
Group D
Group A
Group B
Allocation concealment
Our population
YOU!
List generation
Head: Allowed to ask questions (A)
Tail: NOT Allowed to ask questions (NA)
Allocation concealment
P OR
Listgeneration
Allocationconcealment
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
Intention-to-treat
Teaching method 1
Dead
Alive
Alive
Dead
Dead
Alive
Dead
Alive
R
Treatment 1
Treatment 2
Dead
Alive
Alive
Dead
Dead
Alive
Dead
Alive
R
Treatment 1
Treatment 2
Dead
Alive
Alive
Dead
Dead
Alive
Dead
Alive
Dead
Alive
Alive
Dead
Dead
Alive
Dead
Alive
R
Treatment 1
Treatment 2
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
Intention-to-treat… Why??
Preserves balance between the groups
Reflects real life
Blinding
Patient CaregiverCollector
of outcomedata
Adjudicatorof outcome
Data analyst
5 volunteers!
The authors should tell us who was blinded!
Blinding
P OR
Listgeneration
Allocationconcealment
Blinding
Blinding
Trials with inappropriate blinding are also associated with larger estimates of effect, but not as much as with inappropriate allocation concealment
Allocation Concealment
Blinding
Who? Enroller
Patients, caregivers, data
collectors, adjudicators,
analysts
What? The listGroup
assignments
When?Part of
randomizationAfter
randomization
Allocation Concealment x blinding
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
Therapy Math
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
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
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
Relative Risk Reduction
Teaching method 1
20
By how much (in %) did I reduce?
Answer: 25%
15
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
Relative Risk Reduction
Teaching method 2 Formula: RRR = 1 - RR
Intensive Usual
23%
41%
23% / 41% = 0.56 Risk ratio
Relative Risk Reduction
Teaching method 2 Formula: RRR = 1 - RR
Intensive Usual
23%
41%
23% / 41% = 0.56 Risk ratio
41%
41% / 41% = 1
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%)
Math time!
Mortality
Intensive Usual
2.8%
12%
Risk difference: 9.2%Risk Ratio: 0.23
NNT: 11RRR: 0.77 (77%)
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!
Teaching Take-home Points
• What strategies did we use to teach these concepts?
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”
Back to our patient…
Two months after discharge:
Thank you!