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A FRAMEWORK FOR COST-EFFECTIVENESS ANALYSIS OF PERSONALIZED MEDICINE CO-DEPENDENT TECHNOLOGIES
CADTHApril 12, 2016
Philip Akude – MSc, Reza Mahjoub – PhD, Mike Paulden – MSc, Christopher McCabe – PhD
University of Alberta
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.
OUTLINEBackgroundObjectiveGeneral model descriptionSimplified model with probability of response set exogenouslyOptimal cut-offs for the general model under perfect informationConclusions
CURRENT HTA PRACTICEHTA 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.
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.
CO-DEPENDENT TECHNOLOGIES FLOWCHART
Genotypic Test
(Test d)Positive? Yes
No
No Treatme
nt
Therapy Responde
r Test(Test π)
TP? Yes
FP
No Treatme
nt
Test π
Π≥ΠCYes
No
Stand. Care
Phenotypic Test
(Test u)UR≥UC
Yes
No
Stand. Care
New Tx
TN
MODEL PARAMETERS: Health benefit (phenotype) resulting from the new treatment or standard care, 1,
{ , , Responding, Non-responding, Standard Care}
: Expected health benefit, { , , }: Cost of treatme
i i
i
i
U U
i R N S R N S
U i R N SC
nt/standard care per unit time, { , , }
: Expected cost, { , , }: Cost of test, { , , }
: Error in test measurment; ~ (0, ), { , };ˆ : Observed health benefit(phenotypic expression) f
i
tj
j j j
R
i R N S
C i R N SC j d u
N j u
U
ˆor a responding patient; ˆ ˆ: Estimated probability of response,
: Inverse of CE ratio
R R uU U
g
INCORPORATING HETEROGENEITY1. 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
2. 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
DECISION TREE FOR CO-DEPENDENT TECHNOLOGIES
( )R R td t tuU g C C C C
( )S S td t tuU g C C C C
( )R R td t tuU g C C C C
( )S S td t tuU g C C C C
Cˆ
1
ˆC
1 c
C
ˆC
1R R CuU U U
R C uU U
New Tx
Stand. Care
ˆR CU U
ˆR CU U
1R R CuU U U
R C uU U
New Tx
Stand. Care
ˆR CU U
ˆR CU U
Stand. Care
p
1 p
f
1 f
q
1 q
( )SN SN tdU g C C
( )HN HN td tU g C C C
( )HN HN tdU g C C
Test d
Sick
Healthy
TP
Test
TPE NB
FN
FP
TN
Test 2
Do Not Treat
Do Not Treat
Responding
Not Responding
R CU U
R CU U
( )S S td tU g C C C
( )R R td t tuU g C C C C
( )S S td t tuU g C C C C
( )R R td t tuU g C C C C
( )S S td t tuU g C C C C
ˆC
1 c
C
ˆC
1R R CuU U U
R C uU U
New Tx
Stand. Care
ˆR CU U
ˆR CU U
1R R CuU U U
R C uU U
New Tx
Stand. Care
ˆR CU U
ˆR CU U
Stand. Care
R CU U
R CU U
( )S S td tU g C C C
C
ˆ
ˆR R uU U
ˆR R uU U
ˆR R uU U
ˆR R uU U
Phelps & Mushlin Framework
Same tree as on responding
† FP patients will be correctly diagnosed as TN as a result of second test
†
Test u
( )R R td tuU g C C C
( )R R td tuU g C C C
( )S S td tuU g C C C
( )S S td tuU g C C C
1R u R CU U U
1R u R CU U U
R C uU U
R C uU U
R CU U
R CU U
ˆR R uU U
ˆR R uU U
ˆR CU U
ˆR CU U
New Tx
Stand. Care
ˆR CU U
ˆR CU U
New Tx
Stand. CareTPE NB
( )N N td tuU g C C C
( )N N td tuU g C C C
( )S S td tuU g C C C
( )S S td tuU g C C C
1R u R CU U U
1R u R CU U U
R C uU U
R C uU U
R CU U
R CU U
ˆR R uU U
ˆR R uU U
ˆR CU U
ˆR CU U
New Tx
Stand. Care
ˆR CU U
ˆR CU U
New Tx
Stand. Care
1
p
1 p
f
1 f
q
1 q
Test 1
Sick
Healthy
TP
FN
FP
TN
Test u
Do Not Treat
Do Not Treat
Phelps & Mushlin Framework
( )SN SN tdU g C C
( )HN HN tdU g C C
• Π exogenous• Imperfect information (error in measurement)• One test for magnitude of response UR
Test u
WHEN THE PROBABILITY OF RESPONSE IS EXOGENOUS
OPTIMALITY CONDITION FOR UC
1
0FOC: 0,
where .
TP Ru RR
uuR R RCuC
R N N N NS SR
NBE K f u U f u duU
K u U C Cg U U g C C
1
1
1
1
1
)( uR R Cu RuR R
uR R Cu RuR R
uRu RuR R C
u u Uu u uTP R N R N R Ru u
u u Uu u uN N R Ru u
uu u u R RS Su u U
t
E NB u U g C C f d f u du
U gC f d f u du
U gC f d f u du
g C
.tud C
WORKED EXAMPLE
Us = 0.69UN = 0.35CN = 1,245$ CR = 1,245$ CS = 15,958$ π = 0.6
~ (2.7,0.3) Mean=0.9 and Stand. Deviation=0.15
~ (0,0.1)
R
u
U
N
Example:
*Note that under perfect information, 0.51.CU
* 0.461CU
( )R R td t tuU g C C C C
( )S S td t tuU g C C C C
C
1
New Tx
Stand. Care
p
1 p
f
1 f
q
1 q
( )SN SN tdU g C C
( )HN HN td tU g C C C
( )HN HN tdU g C C
Test 1
Sick
Healthy
TP
Test
TPENB
FN
FP
TN
Test 2
Do Not Treat
Do Not Treat
Not Responding
R CU U
R CU U
Stand. Care ( )S S d tU g C C C C
Phelps & Mushlin Framework
BOTH Π AND UC ARE ENDOGENOUSLY DETERMINED
† FP patients will be correctly diagnosed as TN as a result of second test
†
( )N N td t tuU g C C C C
( )S S td t tuU g C C C C
C
New Tx
Stand. Care
R CU U
R CU U
Stand. Care ( )S S td tU g C C C C
Test u
• No error in measurement
E[NBTP] FOR USE OF TEST
1 1
0
1 1
( ) ( ) ( ) ( ) ( )
( ) ( )
1 ( ) ( ) ( ) ( )
C
R RC C
C
C
R RC C
U
TP R R td t tu u R R S S td t tu u R RU
S S td t
U
N N td t tu u R R S S td t tu u R RU
E NB u g C C C C f u du U g C C C C f u du f d
U g C C C f d
U g C C C C f u du U g C C C C f u du
0
( )
1 ( ) ( )C
S S td t
f d
U g C C C f d
1 1
1
0
( ) 1 ( ) ( ) ( )
( ) ( ) ( ) ( ) ( )
RC C
C C
RC
TP R R td t tu N N td t tu u R RU
U
S S td t S S td t tu u R R
E NB u g C C C C U g C C C C f u f du d
U g C C C f d U g C C C C f u du f d
OPTIMAL CUT OFF CALCULATIONS - UC
1( ) 1 ( ) ( )
( ) ( ) ( )
RC
C
R
TP R R td t tu N N td t tu u R RU
U
S S td t S S td t tu u R R
E NB u g C C C C U g C C C C f u du
U g C C C U g C C C C f u du
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.
* 1( ) ( )C N R N S N S NU U g C C U U g C C
*FOC to find the optimal phenotypic cut off, :
0
C
TP
C
U
E NB
U
1R R N N S Su gC U gC U gC
Net benefits from new TxNet benefits from Stand. Care
OPTIMAL CUT OFF CALCULATIONS - ΠC
*
*
To find the optimal cut-off probability of response :
: 0
At :
C
TP
C
C C
E NBFOC
* * * *
1 1* *
( ) ( )1 ) ( ) ( )
R RC C C C
C R R C N N u R R tu S S u R RU Uu gC U gC f u du gC U gC f u du
CONCLUSIONSThis 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.
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