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Mark Pletcher6/10/2011
Quantifying Treatment Effects
Rationale
Any treatment involves tradeoffs Weigh benefits against risks/costs
Benefit$$ Harm
Rationale
Sometimes the decision is difficult!
Benefit $$ Harm
Rationale
Benefit $$ Harm
How big is this box?
And this one?
Rationale
Tests can help us understand who is most likely to benefit from a treatment
Benefit $$ Harm
How big is this box?
And this one?
Rationale
Tests can help us understand who is most likely to benefit from a treatment Rapid strep to decide who will benefit
from penicillin BNP to decide who will benefit from
furosemide CRP to decide who will benefit from
statins
Rationale
The utility of a test depends on:
How beneficial the treatment is How harmful the treatment is How much the test tells us about
these benefits and harms in a given individual
Risk of harm from the test itself
Rationale
The utility of a test depends on:
How beneficial the treatment is How harmful the treatment is How much the test tells us about
these benefits and harms in a given individual
Risk of harm from the test itselfThe topic for this lecture
Outline
Is an intervention really beneficial? How beneficial is it? Pitfalls Examples
Is the intervention beneficial?
Randomized trials compare an outcome in treated to untreated persons MI in 10% vs. 15% Duration of flu symptoms 3 vs. 5 days
Is the intervention beneficial?
Randomized trials compare an outcome in treated to untreated persons MI in 10% vs. 15% Duration of flu symptoms 3 vs. 5 days
*Statistics* are used to decide if should reject the “null hypothesis” and accept that the intervention is beneficial
Is the intervention beneficial?
But statistics cannot help us interpret effect size
Quantifying the Benefit Effect size
How do we summarize and communicate this?
What is really important for clinicians and policymakers?
Quantifying the Benefit Effect size
How do we summarize and communicate this?
What is really important for clinicians and policymakers?
Example: MI in 10% vs. 15% Q: What do we do with these two
numbers?
Quantifying the Benefit
Two simple possibilities:
10% / 15% = 0.66 15% - 10% = 5%
Quantifying the Benefit
Two simple possibilities:
10% / 15% = 0.66 15% - 10% = 5%
Relative Risk (RR)
Absolute Risk Reduction (ARR)
Quantifying the Benefit
Relative risk as a measure of effect size
RR = 0.66 – is this big or small?
Quantifying the Benefit
Relative risk as a measure of effect size
RR = 0.66 – is this big or small? MI: 10% vs. 15% in
10 years Death: 50% vs. 75% in 3 years Basal Cell CA: 2% vs. 3% in lifetime
Quantifying the Benefit
Relative risk as a measure of effect size
RR = 0.66 – is this big or small? MI: 10% vs. 15% in
10 years Death: 50% vs. 75% in 3 years Basal Cell CA: 2% vs. 3% in lifetime
Medium
Big
Small
Quantifying the Benefit
Relative risk as a measure of effect size
RR = 0.66 – is this big or small? MI: 10% vs. 15% in 10 years Death: 50% vs. 75% in 3 years Basal Cell CA: 2% vs. 3% in lifetime
RR is NOT the best measure of effect size
Quantifying the Benefit
Absolute risk reduction (ARR) is better
ARR = Risk difference = Risk2 – Risk1
Quantifying the Benefit
Absolute risk reduction (ARR) is better
RR ARRMI: 10% vs. 15% in 10 years .66
5%Death: 50% vs. 75% in 3 years .66 25%
Basal Cell CA: 2% vs. 3% in lifetime .66 1%
Q: What does the 34% reduction mean?
Nimotop® Ad Graph
22% 33%
Risk1 = 61/278 = 21.8% Risk2 = 92/276 = 33% RR = 22%/33% = .66 ARR = 33% - 22% = 11%
Nimotop® Ad Graph
22% 33%
Risk1 = 61/278 = 21.8% Risk2 = 92/276 = 33% RR = 22%/33% = .66 ARR = 33% - 22% = 11%
What is 34%?
Nimotop® Ad Graph
22% 33%
Risk1 = 61/278 = 21.8% Risk2 = 92/276 = 33% RR = 22%/33% = .66 ARR = 33% - 22% = 11%
Relative risk reduction (RRR) =
1 – RR = 1-.66 = .34 or 34%
Quantifying the Benefit
RRR is no better than RR
RR RRRMI: 10% vs. 15% in 10 years .66
34%Death: 50% vs. 75% in 3 years .66 34%
Basal Cell CA: 2% vs. 3% in lifetime .66 34%
Quantifying the Benefit
RRR is ALWAYS bigger than ARR (unless untreated risk is 100%)
Quantifying the Benefit
BEWARE of risk reduction language!!
ARR or RRR? “We reduced risk by 34%” “Risk was 34% lower”
Quantifying the Benefit
BEWARE of risk reduction language!!
ARR or RRR? “We reduced risk by 34%” can’t tell “Risk was 34% lower” can’t tell
Very hard to be unambiguous!
Quantifying the Benefit
Another reason that ARR is better:
Translate it into “Number Needed to Treat”
NNT = 1/ARR
Why is NNT = 1/ARR?
67 no stroke anyway
22 strokes with Nimotop®
11 strokes prevented
22 strokes with with treatment
33 strokes with no treatment
100 SAH patients treated
R2
R1
Why is NNT 1/ARR?
Treat 100 SAH patients prevent 11 strokes
Ratio manipulation:
100 treated 1 treated 9.1 treated11 prevented .11 prevented 1
prevented
= =
Why is NNT 1/ARR?
Treat 100 SAH patients prevent 11 strokes
Ratio manipulation:
100 treated 1 treated 9.1 treated11 prevented .11 prevented 1
prevented
= =
1/ARR = NNT
Why is NNT 1/ARR?
NNT best expressed in a sentence:
“Need to treat 9.1 persons with SAH using nimodipine to prevent 1 cerebral infarction”
Quantifying the Benefit
NNT calculation practice
RR ARR NNT?
MI: 10% vs. 15% in 10 years .665%
Death: 50% vs. 75% in 3 years .66 25%
Basal Cell CA: 2% vs. 3% in lifetime .66 1%
Quantifying the Benefit
NNT calculation practice
RR ARR NNT?
MI: 10% vs. 15% in 10 years .665% 20
Death: 50% vs. 75% in 3 years .66 25% Basal Cell CA: 2% vs. 3% in lifetime .66 1%
Quantifying the Benefit
NNT calculation practice
RR ARR NNT?
MI: 10% vs. 15% in 10 years .665% 20
Death: 50% vs. 75% in 3 years .66 25% 4
Basal Cell CA: 2% vs. 3% in lifetime .66 1%
Quantifying the Benefit
NNT calculation practice
RR ARR NNT?
MI: 10% vs. 15% in 10 years .665% 20
Death: 50% vs. 75% in 3 years .66 25% 4
Basal Cell CA: 2% vs. 3% in lifetime .66 1% 100
Quantifying the Benefit
NNT expression practice
RR ARR NNT?
MI: 10% vs. 15% in 10 years .665% 20
Death: 50% vs. 75% in 3 years .66 25% 4
Basal Cell CA: 2% vs. 3% in lifetime .66 1% 100
Statins
Chemo
Sunscreen every day
Quantifying the Benefit
NNT expression practice
“Need to treat 20 patients with statins for 10 years to prevent 1 MI”
“Need to treat 4 patients with chemo for 3 years to prevent 1 death”
“Need to treat 100 patients with sunscreen every day for their whole life to prevent 1 basal cell”
Example 1
Randomized controlled trial of the effects of hip replacement vs. screws on re-operation in elderly patients with displaced hip fractures.
Parker MH et al. Bone Joint Surg Br. 84(8):1150-1155.
Example 1Re-
operationNo Re-
operation
Hip Replacement 12 217 229
Internal Fixation with Screws 90 136 226
Parker MH et al. Bone Joint Surg Br. 84(8):1150-1155.
Example 1Re-
operationNo Re-
operation Risk
Hip Replacement 12 217 229
12/229 = 5.2%
Internal Fixation with Screws 90 136 226
90/226 =
39.8%
Example 1Re-
operationNo Re-
operation Risk
Hip Replacement 12 217 229
12/229 = 5.2%
Internal Fixation with Screws 90 136 226
90/226 =
39.8%
RR = R1/R2 = 5.2% / 39.8% = .13
RRR = 1-RR = 1-.13 = 87%
ARR = R2 – R1 = 39.8% - 5.2% = 34.6%
NNT = 1/ARR = 1/.346= 3
“Need to treat 3 patients with hip replacement instead of screws to prevent 1 from needing a re-do operation”
Example 2
JUPITER: Randomized controlled trial of high dose rosuvastatin in patients with LDL<130 and CRP>2.0
Ridker et al. NEJM 2008; 359:2195-207
Example 2
Ridker et al. NEJM 2008; 359:2195-207
Example 2
Ridker et al. NEJM 2008; 359:2195-207
Example 2
HR = (R1/R2) (from regression) = .56
RRR = 1-HR = 1-.56 = 44%
ARR = R2 – R1 = 1.36 - 0.77 = .59 / 100py*
= .0059 / py
NNT = 1/ARR = 1/.0059 = 100/.59 = 169 pys
“Need to treat 169 patients for a year to prevent 1 CVD event”
Or better:
“Need to treat 85 patients for 2 years to prevent 1 CVD event”
(average treatment duration in trial was 1.9 years)
* py = person-years
Example 4
Warfarin vs. placebo for atrial fibrillation
Warfarin Placebo
Risk of major bleed (/yr) 1.2% 0.7%
Ann Intern Med 1999; 131:492-501
Example 4
Warfarin vs. placebo for atrial fibrillation
RR = R1/R2 = 1.2% / .7% = 1.7
RR (flipped) = R2/R1 = .7% / 1.2% = .59
RRR (flipped) = 1-RR = 1 - .59 = 41%
ARR = R2 – R1 = .7% - 1.2% = -.5%
“ARI” – Absolute risk increase = 0.5%
NNT = 1/ARR = 1/-.5% = -200
“NNH” – Number needed to harm = -NNT = 1/ARI = 200
“If you treat 200 Afib patients with warfarin, you will cause 1 major bleed”
Circling back to test utility… Tests help determine:
If the RR applies Treatment for a disease doesn’t help if you don’t
have the disease! Interactions (RR is higher or lower than average)
Statins more effective if CRP is high? Patients with gene XYZ more likely to have a side
effect
Baseline risk The higher the risk, the larger the ARR, the
smaller the NNT
Key Concepts Test utility depends on how good the
treatment is RR and p-values good for hypothesis
testing/statistics ARR and NNT (and NNH) better for
interpreting clinical importance ARR = risk difference NNT = 1/NNT
Beware RRR and ambiguous language