1. A Framework for Cost-effectiveness Analysis of Personalized Medicine Co-dependent Technologies
Philip Akude – MSc, Reza Mahjoub – PhD, Mike Paulden – MSc, Christopher McCabe – PhD
University of Alberta
CADTH
April 12, 2016

2. Disclosure
This study was conducted under the PACEOMICS project, funded by Genome Canada, Genome Quebec, Genome Alberta and the Canadian Institutes for Health Research (CIHR).
The following authors are funded by PACEOMICS project: Philip Akude, Reza Mahjoub, and Michael Paulden.
Christopher McCabe is funded by the University of Alberta, Faculty of Medicine and Dentistry.

3. Outline Background Objective General model description
Simplified model with probability of response set exogenously
Optimal cut-offs for the general model under perfect information
Conclusions

4. Current HTA Practice
HTA for treatment technologies are increasingly required to have randomized controlled trial evidence of efficacy.
Test technologies are frequently adopted on the basis of evidence of laboratory validity and clinical test performance.
The ascent of personalised medicine, specifically test guided therapies is bringing these two evidentiary traditions together.
Stakeholders of personalized medicine product seek a coherent framework to appraise these technologies.

5. Objective
Develop methods for combining evidence on the test(s) and treatment components of co-dependent technologies, and to identify the cost effective cut offs on the test components for pre-specified values of the willingness to pay for health.

6. Co-dependent Technologies Flowchart
Therapy Responder Test
(Test π)
Genotypic Test
(Test d)
Yes
Yes
Yes
Phenotypic Test
(Test u)
Yes
Positive?
TP?
Π≥ΠC
UR≥UC
New Tx No FP No No
No Treatment
Stand. Care
Stand. Care
Test π TN
No Treatment

7. Model Parameters
Use the general notations and then define the subscripts and mention that we use those subscripts consistently through the prez
Heterogeneity derives the uncertainty in the patient population

8. Incorporating Heterogeneity
The patient population heterogeneous with respect to the “success rate”, i.e., Π:
Fπ(π)=Pr{Π≤π} is CDF of Π and fπ(π) is PDF of Π
The patient population heterogeneous with respect to their phenotypic expression for patients who respond to treatment, i.e., UR:
Fu(uR)=Pr{UR ≤ uR} is CDF of UR and fu(uR) is PDF of UR

9. Decision Tree for Co-dependent Technologies†
† FP patients will be correctly diagnosed as TN as a result of second test

10. When the probability of response is exogenous
Imperfect information (error in measurement)
One test for magnitude of response UR
Adding in measurement errors

11. Optimality Condition for UC

12. Worked Example
Example:

13. Both π and uc are endogenously determined
No error in measurement
Cut-offs are the decision variable and these are the contributions to the decision analysis †
† FP patients will be correctly diagnosed as TN as a result of second test

14. E[NBTP] for use of test

15. Optimal Cut off Calculations - UC
Net benefits from Stand. Care
Net benefits from new Tx
UC* is equal to the clinical expression for a responding patient, at which the payer becomes indifferent between the new treatment and the standard care.

16. Optimal Cut off Calculations - ΠC

17. Conclusions
This study develops a formal decision analytic framework for the economic evaluation of personalised medicine co-dependent technologies.
The method presented offers decision makers the impact a changing cost effectiveness threshold has on the optimal cut-off value for co-dependent technologies.
The optimal test cut-off must be set at the point where the marginal payoff from new treatment is equal to the marginal payoff from standard care.

18. Thank You akude@ualberta.ca