Transcript How to use an article about therapy or prevention

JAMA: Users’ guide to
evidence-based medicine
Therapy 7/9/13
Diagnosis 8/6/13
Harm 11/26/13
Prognosis 12/24/13
How to use an article about
therapy or prevention
MKSAP 16 General Internal Medicine Question 92
A 75-year-old man is hospitalized with sepsis leading to multi-organ failure. A meeting
with family members is convened to discuss goals of care for the patient. The
treatment team, including infectious disease and critical care consultants, has
indicated that the patient is deteriorating despite optimized therapy, and the
prognosis is poor. The daughter brings an Internet printout of a trial of a new
medication for sepsis. The abstract states “We gave drug ‘X’ to 100 consecutive
patients with refractory sepsis in our five intensive care units located in the same
geographic region. Eight percent were alive at 30 days.” Although drug “X” is
marketed in the United States, it is not FDA-approved for treatment of sepsis. A quick
literature search reveals no other studies of drug “X” in the treatment of sepsis.
Which of the following is the main reason that it is difficult to determine the
effectiveness of drug “X” based on the published study?
A.No comparison group
B.Outcome assessment not blinded
C.Patients not randomly assigned to treatment
D.Small study size
The three questions
Are the results of the study valid?
What were the results?
Will the results help me in caring for my
patients?
Are the results valid?
Primary guides:
• Was the assignment of patients to
treatments randomized?
• Were all patients who entered the trial
properly accounted for and
attributed at its conclusion?
– Was follow-up complete?
– Were patients analyzed in the groups to
which they were randomized?
Are the results valid?
Secondary guides:
• Were patients, health workers, and
study personnel blind to treatment?
• Were the groups similar at the start
of the trial?
• Aside from the experimental
intervention, were the groups treated
equally?
Randomization
Clinical outcomes may result from
–
–
–
–
Underlying severity of illness
Presence of comorbid conditions
Known and unknown prognostic factors
Treatment effect
What if there are no randomized trials?
cohort > case control > case series
Did they adjust for confounding variables?
Follow-up
In positive trials, if the number of
patients “lost to follow-up” is large,
assume that:
– All patients in the treatment arm did
badly
– All patients in the control arm did well
If the conclusion would change, the
strength of inference is weakened
Intent-to-treat
As in routine practice, patients in
randomized trials sometimes forget to
take their medicine or even refuse their
treatment altogether.
Non-compliant patients tend to fare
worse, regardless on prognostic factors.
If the study attributes all patients to the
group to which they were randomized, it
is an intent-to-treat analysis
Primary guides of validity
• Randomization
• Complete follow-up
• Intent-to-treat analysis
Blinding
If physicians and patients could not be
blinded (e.g. surgery), then were those
who assess clinical outcomes?
Group similarity
Randomization doesn’t
always produce groups
balanced for known
prognostic factors
Magnitude is important,
statistical significance of the
difference is not
Look for adjustments, and
reasons for adjusting
Equal treatment
“Cointerventions”
interventions other than the treatment
under study, differentially applied to the
treatment and control groups
E.g. giving steroids for a COPD
exacerbation in a study of a β2 agonist
Permissible cointerventions should be
listed, and frequency of administration
documented
Secondary guides of validity
• Blinding
• Group similarity
• Equal treatment of all groups
What were the results?
• How large was the treatment effect?
• How precise was the estimate of the
treatment effect?
Magnitude of effect
In a study of 100 patients, 20% in the control
group died, 15% of the treatment group died
Risk without therapy (Baseline risk):X
20/100=0.20 or 20%
Risk with therapy: Y
15/100=0.15 or 15%
Absolute Risk Reduction (Risk Difference): X – Y 0.20-0.15=0.05
Relative Risk: Y/X
0.15/0.20 = 0.75
Relative Risk Reduction (RRR):
[1-0.75]x100=25%
[1-Y/X] x 100 or [(X-Y) / X] x 100
[0.05/0.20]x100=25%
95% Confidence Interval for the RRR
-38% to +59%
Precision of the estimate
Confidence interval helps interpret both
positive and negative trials
The larger the sample,
the narrower the interval
No CI for RRR?
Examine the p-value
– p=0.05 → lower bound of the 95% CI = 0
Use the standard error (SE)
– upper and lower bounds of the 95% CI
= X ± 2SE
Calculate the 95% CI yourself
–
http://www.graphpad.com/quickcalcs/NNT1.cfm
Will the results help me in
caring for my patients?
• Can the results be applied to my
patient care?
• Were all clinically important outcomes
considered?
• Are the likely treatment benefits worth
the potential harms and costs?
MKSAP 16 General Internal Medicine Question 127
A 50-year-old woman is evaluated for nonischemic cardiomyopathy. Her exercise
tolerance is not limited. She takes an ACE inhibitor daily. She took a β-blocker briefly
but discontinued because of fatigue. Results of the physical examination are normal.
The patient inquires whether she should receive drug “H”. Drug H was studied in 2000
patients ages 40 to 80 years (mean age 63 years) with New York Heart Association
functional class III or IV heart failure. Patients were randomized to receive drug H or a
placebo in addition to usual medications. Eighty percent of patients in the trial also
took a β-blocker and 70% an ACE inhibitor. At the end of 3 years, patients taking drug
H had a significantly reduced rate of a composite outcome of death or heart failure
exacerbations. Approximately 5% of the patients taking drug H had serious adverse
events, compared with 2% in the placebo group.
Which of the following is the main reason why this patient should not be treated with
drug H?
A. her heart failure is too mild
B. she is too young
C. she should be treated with a β-blocker first
D. the drug’s adverse event rate is too high
Does it apply to my patients?
Any compelling reasons why it shouldn’t?
Beware of the subgroup analyses
– Often not planned ahead of time
– “Data mining” in negative studies
– Can use them if:
The difference in effects is very large;
Analysis was specified before the study began;
There was a very small number of subgroups;
Results were replicated in other studies.
Were outcomes clinically
important?
Statins improve lipid profiles
Metformin lowers HgA1c
Isosorbide mononitrate improves CO
Flecainide supresses ventricular
depolarizations
Even when one outcome is clinically
important, watch out for effects on
others (e.g. mortality versus QoL)
MKSAP 16 General Internal Medicine Question 58
A physician is asked to advise the Pharmacy and Therapeutics Committee of the
hospital regarding a new drug to prevent deep venous thrombosis (DVT), drug “Z.”
The physician reviews a recent randomized controlled trial of 5000 patients that
compared drug Z with drug C, which is commonly used and is on the hospital’s
formulary. The following data are abstracted from the trial:
Drug
DVT Cases
Drug Z (n = 2500)
25
Drug C (n = 2500)
50
Based on these data, how many patients need to be treated (number needed to treat,
NNT) with drug Z, compared with drug C, to prevent one extra case of DVT?
A. 1
B. 2
C. 25
D. 100
E. 167
Number needed to treat
Risk with drug C (baseline risk): X
Risk with drug Z (therapy): Y
Absolute Risk Reduction (Risk Difference): X – Y
Relative Risk: Y/X
Relative Risk Reduction (RRR):
[1-Y/X] x 100 or [(X-Y) / X] x 100
95% Confidence Interval for the RRR
Number needed to treat: 1/ARR
50/2500=0.02 or 2%
25/2500=0.01 or 1%
0.02-0.01=0.01 or 1%
0.01/0.02 = 0.5 or 50%
[1-0.5]x100=50%
+19% to +68%
1/0.01=100
Always compare to NNH
The NNT: http://www.thennt.com/
Remember the questions
Are the results of the study valid?
Primary: randomization, follow-up, intent
Secondary: blinding, group similarity and
equality
What were the results?
Effect magnitude (A/RRR) and accuracy (CI)
Will they help me in caring for my patients?
Applicable, clinically important outcomes, NNT