Pharmacometrics uses models based on pharmacology, physiology and disease for quantitative analysis of interactionsbetween drugs and patients.
Often invovles PK, PD and disease progression with a focus on populations.
Benefit Risk Assessment involves the balancing of clinical benefits and risks of a therapeutic intervention.
Often involves several conflicts:Individual versus population healthComplete versus incomplete informationQualitative approach versus quantitative modeling
Pharmacometrics uses models based on pharmacology, physiology and disease for quantitative analysis of interactionsbetween drugs and patients.
Often invovles PK, PD and disease progression with a focus on populations.
Benefit Risk Assessment involves the balancing of clinical benefits and risks of a therapeutic intervention.
Often involves several conflicts:Individual versus population healthComplete versus incomplete informationQualitative approach versus quantitative modeling
4
The BR Challenge in the Drug Approval Process is Well Recognized
The BR Challenge in the Drug Approval Process is Well Recognized
“ The current process of drug approval lacks a systematic approach to benefit-risk analysis, leading to inconsistency, lack of transparency and an inability to challenge or defend decisions.”(Boston Consulting Group, February 2006)
“…in both the pre-approval and post-marketing setting, the risk-benefit analysis that currently goes into regulatory decisions appears to be ad hoc, informal, and qualitative…”(The Future of Drug Safety The Institute of Medicine, 2007)
“ The current process of drug approval lacks a systematic approach to benefit-risk analysis, leading to inconsistency, lack of transparency and an inability to challenge or defend decisions.”(Boston Consulting Group, February 2006)
“…in both the pre-approval and post-marketing setting, the risk-benefit analysis that currently goes into regulatory decisions appears to be ad hoc, informal, and qualitative…”(The Future of Drug Safety The Institute of Medicine, 2007)
5
Some Specific BR Assessment Challenges
Some Specific BR Assessment Challenges
• Benefit risk discussion at the regulators have often been around risk management plans and safety concerns
• Quantitative epidemiology techniques exist for quantifying benefits and risks
• Small but serious risks outweigh considerations of benefit
• Controversy about role of non-RCT data
• Integrating multiple attributes of benefits and risk into a single assessment of BR
• Data gaps always exist
• Benefit risk discussion at the regulators have often been around risk management plans and safety concerns
• Quantitative epidemiology techniques exist for quantifying benefits and risks
• Small but serious risks outweigh considerations of benefit
• Controversy about role of non-RCT data
• Integrating multiple attributes of benefits and risk into a single assessment of BR
• Data gaps always exist
6
Simplified Traditional Regulatory View of BR Simplified Traditional Regulatory View of BR
“Objective” Clinical Trial Data “Subjective” Real Life Evaluation
8
A More Clinically Relevant View of BR Capturing the Patient’s Perspective
A More Clinically Relevant View of BR Capturing the Patient’s Perspective
APPROVE
DISAPPROVE
Minimum acceptable
Efficacy for regulators
Maximum acceptable
risk for regulators
Risk
Benefit
Maximum acceptable
risk for patients
Minimum acceptable
Efficacy for patients
Risk-Benefit Tradeoff Curve
9
OutlineOutline
Framing the Challenges
Methodological Considerations– Measuring Preference– Why Preference Matters: Examples Using Different
Quantitative models
Framing the Challenges
Methodological Considerations– Measuring Preference– Why Preference Matters: Examples Using Different
Quantitative models
10
Quantifying the Trade-Off Between Risk and Benefit
Quantifying the Trade-Off Between Risk and Benefit
If “Risk” and “Benefit” represented two different goods, then the question would be:
How much of “A” are we willing to give up in a trade for “B”
– The ultimate goal is to estimate what is a fair exchange– In most transactions this done quantitatively using a
conversion factor• Exchange rate for currency transactions
There is a growing need to quantify the trade-off between benefits and risk of pharmaceuticals
Benefits and Risk are not in the same unit and a conversion factor is needed to bring these together
– Preference
If “Risk” and “Benefit” represented two different goods, then the question would be:
How much of “A” are we willing to give up in a trade for “B”
– The ultimate goal is to estimate what is a fair exchange– In most transactions this done quantitatively using a
conversion factor• Exchange rate for currency transactions
There is a growing need to quantify the trade-off between benefits and risk of pharmaceuticals
Benefits and Risk are not in the same unit and a conversion factor is needed to bring these together
– Preference
11
Understanding Preference and PerspectiveUnderstanding Preference and Perspective
Preference: Level of satisfaction or desirability that a person associates with a particular health state/outcome
Perspective: Whose preference are we interested in?
Preference varies significantly by perspectiveMeasured using– Health state utility weights – scale anchored in
zero (death) and one (perfect health)– Stated preferences
• Also called discrete choice experiments
Preference: Level of satisfaction or desirability that a person associates with a particular health state/outcome
Perspective: Whose preference are we interested in?
Preference varies significantly by perspectiveMeasured using– Health state utility weights – scale anchored in
zero (death) and one (perfect health)– Stated preferences
• Also called discrete choice experiments
12
Measuring Preferences : Health Utility WeightsMeasuring Preferences : Health Utility WeightsUtilities are numerical representation of the strengths of
an individual’s preference for specific outcomes under conditions of uncertainty
Steps to measuring health utilitiesDefining a set of health states/or health outcomesIdentifying individuals to provide judgments of the desirability of each health stateAggregating across individuals to determine scale values for each health state
Methods that have been used to collect utilities:Standard gamble Time tradeoffVisual Analog Scale
Utilities are numerical representation of the strengths of an individual’s preference for specific outcomes under conditions of uncertainty
Steps to measuring health utilitiesDefining a set of health states/or health outcomesIdentifying individuals to provide judgments of the desirability of each health stateAggregating across individuals to determine scale values for each health state
Methods that have been used to collect utilities:Standard gamble Time tradeoffVisual Analog Scale
13
Measuring Health Utility Weights: Standard Gamble Approach
Measuring Health Utility Weights: Standard Gamble Approach
3 YEAR SURVIVALDECREASING QOL
FULL HEALTH
IMMEDIATE DEATH
PROBABILITY: P
PROBABILITY: 1-P
RESECTION
SURGERY
NO TREATMENT
14
Case Study of Enoxaparin & Low Molecular Weight Heparin for DVT Prophylaxis
Case Study of Enoxaparin & Low Molecular Weight Heparin for DVT Prophylaxis
StudyA Comparison of Low Dose Heparin With Enoxaparin(Low-Molecular-Weight Heparin) As Prophylaxis Against Venous Thromboembolism After Major Trauma
ConclusionsEnoxaparin was more effective than low-dose heparin in preventing venous thromboembolism after major trauma. Both interventions were safe
StudyA Comparison of Low Dose Heparin With Enoxaparin(Low-Molecular-Weight Heparin) As Prophylaxis Against Venous Thromboembolism After Major Trauma
ConclusionsEnoxaparin was more effective than low-dose heparin in preventing venous thromboembolism after major trauma. Both interventions were safe
Source: Geerts WH, Jay RM, Code KI, Chen E, Szalai JP, Saibil EA, Hamilton PA. New England Journal of Medicine 1996; 335: 701-707.
15
Results from the LMWH StudyResults from the LMWH Study
2.3% (2.9-0.6)(in favor of heparin)
5/171(2.9%)
1/173(0.6%)
Major bleeding
Risk
13% (44.1-31)(in favor of enoxaparin)
40/129(31.0%)
60/136(44.1%)
Deep-vein thrombosis
Benefit
ARD(Absolute Risk Difference)
Enoxaparin(LMWH)
Low-dose heparin
Number Needed to Treat (NNT) = 1/ 0.13 = 7.7Number Needed to Harm (NNH)= 1/ 0.023 = 43.5
16
BR Consideration using NNT vs NNH: Treatment benefits outweigh the risks if NNT < NNH
BR Consideration using NNT vs NNH: Treatment benefits outweigh the risks if NNT < NNH
RV = Dependent on the health state utilities for a major bleed & DVT RV = (1- utility of bleed)/ (1- utility of DVT) or (disutility of bleed/ disutility of DVT)
RV = Dependent on the health state utilities for a major bleed & DVT RV = (1- utility of bleed)/ (1- utility of DVT) or (disutility of bleed/ disutility of DVT)
NNH = 43.5(willing to accept 1
bleed to avoid 1 DVT)
NNT = 7.7NNH = 8.7
(43.5/5)(willing to accept 1
bleed to avoid 5 DVTs)
RV=1
RV=5
NNT = 7.7
17
BR Consideration using the Risk Benefit Plane Approach
BR Consideration using the Risk Benefit Plane Approach
μ =1/ RV acceptability threshold of 1
implies one is willing to accept one major bleed
to avert one DVT
-0.050
0.000
0.050
0.100
0.150
0.200
-0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30
Incremental Benefit
Incr
emen
tal R
isk
µ=0.25
µ=1
µ=0.5
0.191 - point estimate of RB ratio
Lynd L, O’Brien B. Journal of Clinical Epidemiology 2004;57:795
18
Benefit - Risk Ratio Point Estimate (Lynd and O’Brien 2004)
Benefit - Risk Ratio Point Estimate (Lynd and O’Brien 2004)
-0.050
0.000
0.050
0.100
0.150
0.200
-0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30
Incremental Benefit
Incr
emen
tal R
isk
µ=0.25
µ=1
µ=0.5
point estimate of RB ratio
μ =1/ RV acceptability threshold of 1
implies one is willing to accept one major bleed
to avert one DVT
Willing to accept 1 major bleed to avoid 4 DVTs
Lynd L, O’Brien B. Journal of Clinical Epidemiology 2004;57:795
19
Limitations of Health State Utilities in BR Assessment
Limitations of Health State Utilities in BR Assessment
• Health state utilities assume that every patient will behave alike in any given situation
• Health state utilities force subjects to trade-off clinically unrealistic scenarios
• perfect health versus instant painless death
• Health state utilities do not allow for patients to adapt their level of risk tolerance along a curve of expected benefits
• Health state utilities assume that every patient will behave alike in any given situation
• Health state utilities force subjects to trade-off clinically unrealistic scenarios
• perfect health versus instant painless death
• Health state utilities do not allow for patients to adapt their level of risk tolerance along a curve of expected benefits
20
Measuring Preferences : Stated Choice Experiments
Measuring Preferences : Stated Choice Experiments
• Requires explicit trade off between the multiple attributes of products or choices.
• The principle that applies is that the preference for any product or choice is based upon an interplay between the various attributes of the product.
• Steps to estimating stated choice preferences• Develop a stated choice survey instrument based upon known
attributes of the product• Estimate the relative importance of each attribute to the
patient’s decision• Estimate the willingness of the patient to accept a particular
risk relative to the magnitude of benefit they expect to achieve
• Requires explicit trade off between the multiple attributes of products or choices.
• The principle that applies is that the preference for any product or choice is based upon an interplay between the various attributes of the product.
• Steps to estimating stated choice preferences• Develop a stated choice survey instrument based upon known
attributes of the product• Estimate the relative importance of each attribute to the
patient’s decision• Estimate the willingness of the patient to accept a particular
risk relative to the magnitude of benefit they expect to achieve
21
Stated Choice Experiment: Natalizumab in Crohn’s Disease
Stated Choice Experiment: Natalizumab in Crohn’s Disease
Conjoint trade-off tasks involving several treatment scenarios– Several efficacy and risk levels tested
Treatment features included indirect clinical benefits– Daily symptoms and activity limitations of CD patients– Risk of flares– Diminishing exposure to steroids– Risk of SAEs (PML, Lymphoma, Opportunistic infections)
Maximum acceptable risk (MAR) for each SAE was measured for different levels of clinical benefit
– Johnson FR et al Gastroenterology 2007;133:769-779
Conjoint trade-off tasks involving several treatment scenarios– Several efficacy and risk levels tested
Treatment features included indirect clinical benefits– Daily symptoms and activity limitations of CD patients– Risk of flares– Diminishing exposure to steroids– Risk of SAEs (PML, Lymphoma, Opportunistic infections)
Maximum acceptable risk (MAR) for each SAE was measured for different levels of clinical benefit
– Johnson FR et al Gastroenterology 2007;133:769-779
22
Natalizumab: Relative Importance of Each Attribute to Patient Satisfaction
Natalizumab: Relative Importance of Each Attribute to Patient Satisfaction
0.00
0.10
0.20
0.30
0.40
Severity Risk of PML Risk ofLymphoma
Risk ofInfection
Effect onComplications
Steroids Time to NextFlare-up
Rel
ativ
e Im
port
ance
–Johnson FR et al Gastroenterology 2007;133:769-779
23
PML Serious Infection Lymphoma
Mean(Lower Bound, Upper Bound)
Mean(Lower Bound, Upper Bound)
Mean(Lower Bound, Upper Bound)
Severe Remission 0.70%(0.60, 0.80)
0.73%(0.66, 0.81)
0.82%(0.72, 0.92)
Severe Mild 0.61%(0.53, 0.70)
0.67%(0.61, 0.73)
0.73%(0.64, 0.81)
Severe Moderate 0.19%(0.11, 0.28)
0.28%(0.02, 0.54)
0.39%(0.25, 0.52)
Moderate Remission 0.39%(0.27, 0.52)
0.55%(0.48, 0.61)
0.55%(0.48, 0.62)
Moderate Mild 0.22%(0.14, 0.30)
0.37%(0.17, 0.57)
0.42%(0.33, 0.52)
Initial Health State
Final Health State
Natalizumab: Maximum Acceptable Risks for Select Treatment Benefits
Natalizumab: Maximum Acceptable Risks for Select Treatment Benefits
–Johnson FR et al Gastroenterology 2007;133:769-779
24
ConclusionConclusion
• BR modeling much like Pharmacometrics relies upon extrapolating data observed in RCTs to make decisions beyond what was actually observed
• This requires making tradeoffs between attributes not naturally measured in the same units
• Estimating preferences/utilities is necessary to make the trade-offs but has limitations
• BR modeling much like Pharmacometrics relies upon extrapolating data observed in RCTs to make decisions beyond what was actually observed
• This requires making tradeoffs between attributes not naturally measured in the same units
• Estimating preferences/utilities is necessary to make the trade-offs but has limitations
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