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Methods to Analyse The Economic Benefits of a Pharmacogenetic (PGt) Test to Predict Response to Biologic Therapy in Rheumatoid Arthritis, and to Prioritise Further Research. Alan Brennan 1 , Nick Bansback 1 , 1 ScHARR, University of Sheffield, England.

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slide1
Methods to Analyse The Economic Benefits of a Pharmacogenetic (PGt) Test to Predict Response to Biologic Therapy in Rheumatoid Arthritis, and to Prioritise Further Research

Alan Brennan1, Nick Bansback1,

1ScHARR, University of Sheffield, England.

Kip Martha2, Marissa Peacock2, Kenneth Huttner2

2Interleukin Genetics, Inc.

biologics
“Biologics”

Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)*

*Costs include monitoring

Anakinra 100mg

Etanercept 25mg eow

Infliximab 3mg/kg 8 weekly

biologics1
“Biologics”

Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)*

Cytokines

Interleukin 1 TNF alpha TNF Alpha

*Costs include monitoring

Anakinra 100mg

Etanercept 25mg eow

Infliximab 3mg/kg 8 weekly

biologics2
“Biologics”

Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)*

Is Response Genetic?

91 patients, 150mg Anakinra, 24 week RCT1,2, gene = IL-1A +4845

Positive response = reduction of at least 50% in swollen joints

1 Camp et al. American Human

Genetics Conf abstract 1088, 1999

2 Bresnihan

Arthritis & Rheumatism, 1998

*Costs include monitoring

Anakinra 100mg

Etanercept 25mg eow

Infliximab 3mg/kg 8 weekly

biologics3
“Biologics”

Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)*

Is Response Genetic?

24 week RCT1,2 , 91 patients, 150mg Anakinra,, gene = IL-1A +4845

Defined response = reduction of at least 50% in swollen joints

1 Camp et al. American Human

Genetics Conf abstract 1088, 1999

2 Bresnihan

Arthritis & Rheumatism, 1998

*Costs include monitoring

Anakinra 100mg

Etanercept 25mg eow

Infliximab 3mg/kg 8 weekly

biologics4
“Biologics”

Anakinra ($12,697), Etanercept ($18,850), Infliximab ($24,112)*

Is Response Genetic?

91 patients, 150mg Anakinra, 24 week RCT1,2, gene = IL-1A +4845

Positive response = reduction of at least 50% in swollen joints

1 Camp et al. American Human

Genetics Conf abstract 1088, 1999

2 Bresnihan

Arthritis & Rheumatism, 1998

*Costs include monitoring

Anakinra 100mg

Etanercept 25mg eow

Infliximab 3mg/kg 8 weekly

100%

50%

50%

health outcomes
Health Outcomes
  • ACR20 response

-20% in swollen, and tender joints, and in 3 other measures

health outcomes1
Health Outcomes
  • ACR20 response

-20% in swollen, and tender joints, and in 3 other measures

ACR20 = 0.88 * Swollen50 score (trial data)

health outcomes2
Health Outcomes
  • ACR20 response

-20% in swollen, and tender joints, and in 3 other measures

ACR20 = 0.88 * Swollen50 score (trial data)

Response ==> symptom relief and delayed progression long term

health outcomes3
Health Outcomes
  • ACR20 response

-20% in swollen, and tender joints, and in 3 other measures

ACR20 = 0.88 * Swollen50 score (trial data)

Response ==> symptom relief and delayed progression long term

  • “Years in ACR20 Response” = primary outcome

3 Kobelt et al. Economic Conseque of Progression of RA in Swe. A&R 1999

health outcomes4
Health Outcomes
  • ACR20 response

-20% in swollen, and tender joints, and in 3 other measures

ACR20 = 0.88 * Swollen50 score (trial data)

Response ==> symptom relief and delayed progression long term

  • “Years in ACR20 Response” = primary outcome
  • ACR 20 Response  0.8 reduction in HAQ (0 to 3 scale)
  • Utility  0.86 - 0.2 * HAQ 3

3 Kobelt et al. Economic Conseque of Progression of RA in Swe. A&R 1999

2 year treatment sequence pathway
2 Year Treatment Sequence Pathway
  • Initial Response Longer term discontinuation
a pharmaco genetic strategy
A Pharmaco-Genetic Strategy

Strategy 1

Strategy 2

strategy sequences to compare
1

2

3

0

Strategy Sequences to Compare

A Anakinra

PGt Genetic

E Etanercept

I Infliximab

- Maintenance

cost assumptions
Cost Assumptions
  • Drugs and Monitoring
  • Other Healthcare  HAQ$Cost pa = $1,084 + $1,636 * HAQ 4 ==> Responder = $ 2,400 pa Non Responder = $ 3,700 pa
  • PGt = $200
  • Excluding :Nursing Home Care, Employer Costs
  • No uncertainty analysis

4 Yelin and Wanke . A&R 1999………...

2 level evsi research design 4 5
2 Level EVSI - Research Design4, 5

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 51
2 Level EVSI - Research Design4, 5

0)Decision model, threshold, priors for uncertain parameters

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 52
2 Level EVSI - Research Design4, 5

0)Decision model, threshold, priors for uncertain parameters

1) Simulate data collection:

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 53
2 Level EVSI - Research Design4, 5
  • 0)Decision model, threshold, priors for uncertain parameters
  • 1) Simulate data collection:
  • sample parameter(s) of interest once ~ prior
  • (1st level)

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 54
2 Level EVSI - Research Design4, 5
  • 0)Decision model, threshold, priors for uncertain parameters
  • 1) Simulate data collection:
  • sample parameter(s) of interest once ~ prior
  • decide on sample size (ni) (1st level)

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 55
2 Level EVSI - Research Design4, 5
  • 0)Decision model, threshold, priors for uncertain parameters
  • 1) Simulate data collection:
  • sample parameter(s) of interest once ~ prior
  • decide on sample size (ni) (1st level)
  • sample a mean value for the simulated data | parameter of interest

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 56
2 Level EVSI - Research Design4, 5
  • 0)Decision model, threshold, priors for uncertain parameters
  • 1) Simulate data collection:
  • sample parameter(s) of interest once ~ prior
  • decide on sample size (ni) (1st level)
  • sample a mean value for the simulated data | parameter of interest

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 57
2 Level EVSI - Research Design4, 5
  • 0)Decision model, threshold, priors for uncertain parameters
  • 1) Simulate data collection:
  • sample parameter(s) of interest once ~ prior
  • decide on sample size (ni) (1st level)
  • sample a mean value for the simulated data | parameter of interest
  • 2) combine prior + simulated data --> simulated posterior

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 58
2 Level EVSI - Research Design4, 5
  • 0)Decision model, threshold, priors for uncertain parameters
  • 1) Simulate data collection:
  • sample parameter(s) of interest once ~ prior
  • decide on sample size (ni) (1st level)
  • sample a mean value for the simulated data | parameter of interest
  • 2) combine prior + simulated data --> simulated posterior
  • 3) now simulate1000 times
  • parameters of interest ~ simulated posterior
  • unknown parameters ~ prior uncertainty(2nd level)

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 59
2 Level EVSI - Research Design4, 5
  • 0)Decision model, threshold, priors for uncertain parameters
  • 1) Simulate data collection:
  • sample parameter(s) of interest once ~ prior
  • decide on sample size (ni) (1st level)
  • sample a mean value for the simulated data | parameter of interest
  • 2) combine prior + simulated data --> simulated posterior
  • 3) now simulate1000 times
  • parameters of interest ~ simulated posterior
  • unknown parameters ~ prior uncertainty(2nd level)
  • 4) calculate best strategy = highest mean net benefit

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 510
2 Level EVSI - Research Design4, 5
  • 0)Decision model, threshold, priors for uncertain parameters
  • 1) Simulate data collection:
  • sample parameter(s) of interest once ~ prior
  • decide on sample size (ni) (1st level)
  • sample a mean value for the simulated data | parameter of interest
  • 2) combine prior + simulated data --> simulated posterior
  • 3) now simulate1000 times
  • parameters of interest ~ simulated posterior
  • unknown parameters ~ prior uncertainty(2nd level)
  • 4) calculate best strategy = highest mean net benefit
  • 5) Loop 1 to 4 say 1,000 times Calculate average net benefits

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 511
2 Level EVSI - Research Design4, 5
  • 0)Decision model, threshold, priors for uncertain parameters
  • 1) Simulate data collection:
  • sample parameter(s) of interest once ~ prior
  • decide on sample size (ni) (1st level)
  • sample a mean value for the simulated data | parameter of interest
  • 2) combine prior + simulated data --> simulated posterior
  • 3) now simulate1000 times
  • parameters of interest ~ simulated posterior
  • unknown parameters ~ prior uncertainty(2nd level)
  • 4) calculate best strategy = highest mean net benefit
  • 5) Loop 1 to 4 say 1,000 times Calculate average net benefits
  • 6) EVSI parameter set = (5) - (mean net benefit | current information)

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

2 level evsi research design 4 512
2 Level EVSI - Research Design4, 5
  • 0)Decision model, threshold, priors for uncertain parameters
  • 1) Simulate data collection:
  • sample parameter(s) of interest once ~ prior
  • decide on sample size (ni) (1st level)
  • sample a mean value for the simulated data | parameter of interest
  • 2) combine prior + simulated data --> simulated posterior
  • 3) now simulate1000 times
  • parameters of interest ~ simulated posterior
  • unknown parameters ~ prior uncertainty(2nd level)
  • 4) calculate best strategy = highest mean net benefit
  • 5) Loop 1 to 4 say 1,000 times Calculate average net benefits
  • 6) EVSI parameter set = (5) - (mean net benefit | current information)

4 Brennan et al Poster

SMDM 2002

5 Brennan et al Poster

SMDM 2002

results 6 months
Results - 6 months

4 strategies: A, E, I and PGt

results 6 months1
Results - 6 months

4 strategies: A, E, I and PGt

results 6 months2
Results - 6 months

4 strategies: A, E, I and PGt

base case results 2 years
Base-case Results - 2 years

20 strategies: A, E, I and PGt sequences

base case results 2 years1
Base-case Results - 2 years

20 strategies: A, E, I and PGt sequences

Optimal Strategy

Depends on Threshold:

$10k ==> maintenance therapy (20)

$20k ==> sequence of 2 biologics (11)

$25k ==> PGt + 2 biologics (9)

$30k ==> PGt + 3 biologics (19)

base case results 2 years2
Base-case Results - 2 years

20 strategies: A, E, I and PGt sequences

Optimal Strategy Prob

Depends on Threshold: Optimal

$10k ==> maintenance therapy (20) 100%

$20k ==> sequence of 2 biologics (11) 42%

$25k ==> PGt + 2 biologics (9) 18%

$30k ==> PGt + 3 biologics (19) 43%

incorporating uncertainty
Incorporating Uncertainty
  • Assuming 25,000 per annum new patients starting biologics over next 5 years
partial evsi pgt research only
Partial EVSI: PGt Research only

Caveat: Small No.of Simulations on 1st Level

interleukin genetics inc target ra program
Interleukin Genetics Inc. TARGET RA program
  • Conceptual modelling identified key missing data and helped prioritise further primary data collection

1. PGt test performance (increased sample size).

2. Etanercept / Infliximab performance in gene subgroups

3. Anakinra response rate in anti-TNFα failures

conclusions
Conclusions
  • Early economic evaluation suggests potential for a cost-effective pharmacogenetic test.
conclusions1
Conclusions
  • Early economic evaluation suggests potential for a cost-effective pharmacogenetic test.
  • Expected value of information analysis has quantified the key research priorities.
conclusions2
Conclusions
  • Early economic evaluation suggests potential for a cost-effective pharmacogenetic test.
  • Expected value of information analysis has quantified the key research priorities.
  • EVSI can quantify the value of the specific research design
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