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1 Surrogat - endepunkter: Teori og empiri Fagdag i helseøkonomi 3. mars 2009 Ivar Sønbø Kristiansen ([email protected] ) Institutt for helseledelse og helseøkonomi, UiO Institut for Sundhedstjenesteforskning, Syddansk Universitet, Odense

Surrogat - endepunkter: Teori og empiri

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Surrogat - endepunkter: Teori og empiri. Fagdag i helseøkonomi 3. mars 2009 Ivar Sønbø Kristiansen ( [email protected] ) Institutt for helseledelse og helseøkonomi, UiO Institut for Sundhedstjenesteforskning, Syddansk Universitet, Odense. Conflict of interest. - PowerPoint PPT Presentation

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Page 1: Surrogat - endepunkter: Teori og empiri

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Surrogat - endepunkter:Teori og empiri

Fagdag i helseøkonomi 3. mars 2009Ivar Sønbø Kristiansen ([email protected])

Institutt for helseledelse og helseøkonomi, UiO

Institut for Sundhedstjenesteforskning, Syddansk Universitet, Odense

Page 2: Surrogat - endepunkter: Teori og empiri

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Conflict of interest

• Institutt for helseledelse og helseøkonomi har mottatt et doktorgradsstipend av Legemiddelindustriforeningen for blant annet å studere validiteten av surrogat-endepunkter

Page 3: Surrogat - endepunkter: Teori og empiri

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Surrogate endpoint

• ”Any end point that substitutes for and predicts a final patient-related outcome”

• May lead to shorter and smaller studies and faster times to licensing and dissemination of new technologies

(Taylor-RS and Elston-J. Health Technology Assessment 2009; 13: No. 8)

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Surrogate endpoints

(Taylor-RS and Elston-J. Health Technology Assessment 2009; 13: No. 8)

Disease Surrogate endpoint

Clinical endpoint

HIV infection CD4 count AIDS or death

Colorectal cancer Tumour progression

Life years

Cardiovascular disease

Blood pressure Cholesterol level

Life years

Gaucoma Introcular pressure Vision loss

Osteoporosis Bone mineral density

Bone fracture

Page 5: Surrogat - endepunkter: Teori og empiri

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Surrogate endpoint ”successes”

• Coronary Heart Disease Policy Model (Goldman et al., 1991) corroborated by 4S trial of statins in secondary prevention

• Others

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Surrogate endpoint failures

• AZT treatment for HIV/AIDS • Premature ventricular beats: CAST I/II trials

(encainide, flecainide, moricizine)• WHO clofibrate study

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• RCT clofibrate versus placebo (n=3,898)• 5-year mortality: 20.9% vs 20.9% (p=0.55)• Clofibrate 80%+ dose: 15.0% mortality• Clofibrate 0-79% dose: 24.6% mortality• Placebo 80%+ dose: 15.1% mortality• Placebo 0-79% dose: 24.6% mortality• Placebo-difference adjusted for 40 explanatory

variables: 16.4% vs 25.8% (p<0.000000007)

Cholesterol as surrogate endpoint

NEJM 1980; 303: 1038-41)

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Perfect surrogate endpoints

Surrogate endpoint

Clinical outcome

One single causal link

Page 9: Surrogat - endepunkter: Teori og empiri

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Perfect surrogate endpoints

Surrogate endpoint

Clinical outcome

One single causal link

Page 10: Surrogat - endepunkter: Teori og empiri

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Perfect surrogate endpoints

Surrogate endpoint

Clinical outcome

Rabies

One single causal link

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Reasons for surrogate endpoint failure

(Taylor-RS and Elston-J. Health Technology Assessment 2009; 13: No. 8)

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Prentice-kriteriene

• “For a valid surrogate endpoint, a test of the null hypothesis of no relationship to the treatment groups for the surrogate endpoint is also a valid test of the corresponding null hypothesis for the true clinical outcome.”

• Prentice anførte fire kriterier for at dette kravet skal være oppfylt. Disse benyttes lite i dag da de er ansett å være for strenge, men teoretisk sett optimale

(Prentice. Surrogate endpoints in clinical trials: definition and operational criteria.

Statistics in medicine 1989;8(4):431)

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Prentice-kriteriene

• f(S|T) ≠ f(S) (treatment affects distr. surrogate)• f(C|T) ≠ f(C) (treatment affects clinical

outcome)• f(C|S) ≠ f(C) (surrogate affects clinical

outcome)• f(C|S,T) = f(C|S) (all treatment effects go

through surrogate)

S = SurrogatC = Clinical outcomeT = Treatment

(Prentice. Surrogate endpoints in clinical trials: definition and operational criteria.

Statistics in medicine 1989;8(4):431)

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Validering i praksis

• Det vanlige i praksis er å lage en regresjonsmodell:

C = f(a+bS)

• Beregner a og b ved en eller annen regresjonsteknikk.• a=0 betyr at bare S påvirker C (Prentice kriterium 4) • b ≠ 0 betyr at S påvirker C (Prentice kriterium 1-3)

• Individ-data versus aggregerte data• Mange meta-regresjoner på kreft, få på hjertekarsykdom• Medline: 50,000 hits for RCT and

(hypercholesterolaemia or hypertension)Håvard Andre Larsen et al: Blood pressure as surrogate endpoint in antihypertensive treatment: A Bayesian meta regression (unpublished)

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Validering i praksisdatagrunnlag

• Kunnskapssenterets rapport om medikamentell primærprevensjon med antihypertensiva og kolesterolsenkende medikamenter

• 29 studier på hypertensjon• Ekskluderte studier med diabetes og/eller

behandlingsskifte i studien, eller manglende info om varians i surrogat og/klinisk endepunkt (n=20)

• Surrogat-endepunkt: blodtrykk• Klinisk endepunkt: slag og død (antall slag og

dødsfall)Håvard Andre Larsen et al: Blood pressure as surrogate endpoint in antihypertensive treatment: A Bayesian meta regression (unpublished)

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Validering i praksis: studieneStudy Treatment Ln RR(Stroke)

b + SD(b)

Ln RR(Death)

B SD(b)

BP difference

Mean (SD)

STOP Hydrochlorothazide and triameterene vs placebo

-0.556 (0.20)

-0.599 (0.23)

-22.0 (0.89)

STOP2 ACE-inhibitor vs beta-blocker and diuretics

0.033 (0.067) -0.094

(0.09)

2.14 (0.28)

VALUE Valsartan vs amlodipine

0.021 (0.046)

0.129(0.08)

2.0(0.065)

NORDIL

Dilitiazem vsDiuretic

0.024(0.091)

-0.198(0.11)

3.20(0.105)

SHEP Chlorthalidone vs Placebo

-0.125(0.089)

-0.428(0.12)

-14.0(0.23)

SYST EUR Nitrendipine vs

Placebo-0.151

(0.121)-0.537(0.18)

-9.0(0.24)

ASCOT BPLA Amlodipine vs

Atenolol-0.108

(0.049)-0.257(0.07)

-3.8(0.064)

SCOPE Candersatan vsPlacebo

-0.080(0.14)

-0.546(0.28)

-5.0(0.62)

ALLHAT Amlodipine vs chlorthalidone

-0.040(0.033)

-0.060(0.06)

1.6(0.041)

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Validering i praksis

2 2 2 2 21 1 1 1 1 1 1 2

2 2 2 2 22 2 2 1 2 2 2 2

~ ,i i i i i

i i i i i

N

• Bayesian meta regression with DIC criterion performed in Winbugs

• Poorest fit: model without surrogate• Best fit: model with intercept, covariance

between endpoints included, but between-trial variance=0.

• Surrogate impacts clinical outcomes, but larger SDs for stroke – more uncertain predictions for stroke than mortality

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Validering i praksis

-24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 2 4 6 8

-0.6

-0.4

-0.2

BP difference

LN(RR)

Håvard Andre Larsen et al: Blood pressure as surrogate endpoint in antihypertensive treatment: A Bayesian meta regression (unpublished)

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Prediksjon av effekt av blodtrykksreduksjon på dødelighet

(n=2.000)Blood pressure

difference (mmHg)SE RR

1 0.4 0.99

3 0.4 0.96

5 0.4 0.93

7 0.4 0.91

Håvard Andre Larsen et al: Blood pressure as surrogate endpoint in antihypertensive treatment: A Bayesian meta regression (unpublished)

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RCTs and clinical endpoints

• 324 consecutive CVD trials• JAMA, Lancet, NEJM 2000-2005• Surrogate as primary endpoint: 77/115 (67%)

”positive”• Clinical as primary endpoint: 113/209 (54%,

p=0,02) ”positive”

(Ridker et al. Jama 2006; 295: 2270-4)

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UK HTA study

• UK HTA program monographs 2005-6 (n=100)• 4 based on cost-effectiveness models• Recommendation:

– Primarily clinical relevant endpoints incl HRQOL– Review of the evidence of the surrogate

• Level 1: association in RCTs• Level 2: association in observational studies• Level 3: plausibility based on biological models

(Taylor&Elston: HTA 2009; 13: No. 8

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Tentative konklusjoner

• Surrogatendepunkter kan gi valid prediksjon av kliniske endepunkter

• Surrogatendepunkter behøver ikke gi valid prediksjon av kliniske endepunkter

• Hvorfor skulle man unnlate å gjøre studier med kliniske endepunkter når det likevel genereres lastbil-lass med dokumentasjon?