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Value of InformationValue of Information

Yot Teerawattananon, MDInternational Health Policy Program, Ministry of Public

Health

PhD candidate in Health Economics, University of East Anglia, UK

yot@ihpp.thaigov.net or yot.t@uea.ac.uk

An application in health economic evaluation of renal replacement therapy in Thailand

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Outline

• Policy issues

• Theoretical ground

• A case study

• Conclusions

The policy issues?

A growing attention for the use of economic evaluation for making reimbursement decision

The evaluation needs numerous parameters relating to treatment effects, utilities, resource use and costs.

These parameters contain degree of uncertainties

Reject or accept the technology based on existing evidence

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Decision rules

Maximising social welfare (health gain e.g. QALY)

Comparing expected incremental CE ratio (ICER) with the willingness to pay threshold--ceiling ratio

Accept the technology if ICER < ceiling ratio

Reject the technology if ICER > ceiling ratio

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Decision options

Policy makers are facing one of these four situations in making decision:

Confidently accept the technology base on existing evidence

Confidently reject the technology based on existing evidence

Accept the technology but demand additional research to inform this decision in the future

Reject (or defer decision about) the technology now but demand further research to inform this decision in the future

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Analysis of value of information

1. The expected cost of uncertainty (pop EVPI) the maximum that the health system should be willing to pay for additional information

2. The relative value of information for each model parameters (partial EVPI)

3. The value of an additional sample the optimal sample size for collecting additional data (sample EVPI)

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A B

A B

A B

10£

10£

10£

5£

5£

5£

= ??? £Imperfect information

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A B

A B

A B

10£

10£

10£

5£

5 £

5£

10 £

10 £

5 £

10 £

10 £

5 £

5 £

10 £5 £ = 25 £Imperfect

information

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A B

A B

A B

10 £

10 £

5 £

10 £

10 £

5 £

5 £

10 £10 £ = 30 £Perfect

information

FormulationFormulation

EVPI = EV(perfect information) - EV(current EVPI = EV(perfect information) - EV(current information)information)

EVPI = EEVPI = EθθmaxmaxjjNB(j, θ) - maxNB(j, θ) - maxjjEEθθNB(j, NB(j,

θ)θ)

EVPI = 30EVPI = 30££ - 25 - 25££ = 5 = 5££

Further readings: 1. Ades AE, Lu G, Claxton K. Expected value of sample information calculations in medical decision modeling. Medical Decision Making 2004;24(2):207-27.2. Sculpher M, Claxton K. Establishing the cost-effectiveness of new pharmaceuticals under conditions of uncertainty--when is there sufficient evidence? Value Health 2005;8(4):433-46.

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Net Monetary Benefit (NMB)(NMB=Net benefit*ceiling ratio)

A BMax. NMB

iteration 1 50,000 70,000 70,000iteration 2 40,000 35,000 40,000…………………………………………………………………………iteration 1,000 45,000 60,000 60,000------------------------------------------------------------------------------------Expected(average) 45,000 50,000

65,000

EVPI = EθmaxjNB(j, θ) - maxjEθNB(j, θ)

65,000 - 50,000 = 15,000

Decision model using probabilistic sensitivity analysis

A case study* Aim: to examine value for money for including dialysis services (PD or HD) for chronic renal disease within the public benefit package in Thailand

Method: Cost-utility analysis

Comparator: palliative management (STD)

Approach: Markov model with PSA

Perspective: societal

*Teerawattananon Y, Mugford M, Tangcharoensathien V: Economic evaluation of palliative management vs. peritoneal and hemodialysis for end-stage renal disease: evidences for making coverage decision in Thailand. submitted to journal 'Value in Health' 2005.

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Death

Initial mode of dialysis

Switching to another mode

of dialysis

ESRD patients

complications complications

Without dialysis

Figure 1. Markov model

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0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Ѯ0

Ѯ10,000

Ѯ30,000

Ѯ50,000

Ѯ150,000

Ѯ250,000

Ѯ350,000

Ѯ450,000

Ѯ550,000

Ѯ650,000

Ѯ750,000

Ѯ850,000

Ѯ950,000

Ѯ1,050,000

Ѯ1,150,000

Ѯ1,250,000

Ѯ1,350,000

Ѯ1,450,000

Ѯ1,550,000

Ѯ1,650,000

Ѯ1,750,000

Ѯ1,850,000

Ѯ1,950,000

Ѯ2,050,000

Ѯ2,150,000

Ѯ2,250,000

Ѯ2,350,000

Ѯ2,450,000

Value of ceiling ratio

Pro

ba

bil

ity

of

fav

ou

rin

g e

ac

h t

rea

tme

nt

mo

da

lity

STD Dialysis

Figure 2. Cost-effectiveness acceptability curves

ICER~ 750,000 Baht/QALYICER~ 750,000 Baht/QALY

with 49% of getting a wrong decision!with 49% of getting a wrong decision!

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0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

0 30 150 350 550 750 950 1,150 1,350 1,550 1,750 1,950 2,150 2,350

Value of ceiling ratio (X1,000 Baht)

Exp

ecte

d V

alue

of P

erfe

ct In

form

atio

n (m

illion

Bah

t)

Figure 3. Population EVPI

16

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

0 30 150 350 550 750 950 1,150 1,350 1,550 1,750 1,950 2,150 2,350

Value of ceiling ratio (X1,000 Baht)

Expecte

d V

alu

e o

f Perfe

ct In

form

ation (m

illion B

aht)

Figure 4. Population EVPI

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Value of ceiling ratio

Pro

ba

bility

o

f fa

vo

urin

g e

ac

h tre

atm

en

t m

od

ality

STD Dialysis

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-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

pDoN

ot

pCom

PD

pCom

HD

pHDto

PD

pPDto

HD

Surv

Func

tion

cPali

ative

cChr

oHD

cChr

oPD

cPerito

nitis

cCoM

obid

cIdM

Donot

cIdM

HD

cIdM

PD

cPati

entH

D

cPati

entP

D

uPDno

Com

uHDno

ComuC

om

Par

tial

EV

PI (

milliio

n ba

ht)

Figure 5. Parameter (partial) EVPI

cChroHD = health care cost of hemodialysiscChroPD = health care cost of peritoneal dialysiscCoMobid =health care cost of treating co-morbid conditionsuPDnoCom = utility for PD without complicationuHDnoCom = utility for HD without complication

Conclusions

• Economic evaluation always involves a degree of uncertainty

• Analysis of value of information offers a way to determine whether additional research is required and at what cost?

• It is valuable because it shows the need for setting research priorities

Acknowledgement

Fellowship program of World Health Organization

National Health Security Office, Thailand

Prof. Miranda Mugford & Dr. Steve Russell, University of East Anglia, UK