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!