extrapolation of trial-based survival curves: constraints based on external information bayes2014,...
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Extrapolation of trial-based survival curves: constraints based
on external information
BAYES2014, University College London, London11th- 13th June 2014
Patricia Guyot1,2, Nicky J Welton1, AE Ades1
Thanks to: M Beasley3
1School of Social and Community Medicine, University of Bristol2Mapi Consultancy3Bristol Haematology and Oncology Centre
Why Extrapolate Survival Curves?
• Health Technology Assessment requires a comparison of the expected quality-adjusted life-years between different technologies• A key element is difference in life expectancy
• End-Of-Life criterion also require estimates of: • life expectancy• gains in life expectancy
Life Expectancy Difference
• Difference in mean survival times• Can be calculated as the difference in areas
between the curves over lifetime • But trials typically follow-up for just a few years• Mean survival times very sensitive to
assumptions on what happens after the trial follow-up (in the “tails” of the curves)
Cetuximab+Radiotherapy vs Radiotherapy for Head and Neck Cancer
• NICE TA145 June 2008
• Bonner et al (2006) trial
• 5-year follow-up
Overall Survival (Bonner et al 2006)
?
Overall Survival (Bonner et al 2006)
?
Overall Survival (Bonner et al 2006)
?!
How to Extrapolate?• Need to assume something about:
• the survival time distribution• Eg: Exponential, Weibull, Log-Normal ...• Cox models don’t help with this
• the hazard ratio • proportional hazards (constant hazard ratio)• increasing or decreasing hazard ratio• “bath-tub” hazard ratio
• Helps to have individual patient data, or sufficient statistics to explore alternative curves
Recontructing data from published Kaplan-Meier curves
• Guyot et al. (2012) method to approximate the data used to produce kaplan-meier curves
• Inputs:• Uses software to obtain co-ordinates from image from
a .pdf file (we used digitizeit)• Numbers at risk published below the curve (defines
fixed number of intervals)• Total number of deaths/events (if reported)
Cetuximab: Locoregional Disease Control
Original publicationReconstructed KM data
0 10 20 30 40 50 60
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Pro
po
rtio
n A
live
Cetuximab+Radiotherapy
Radiotherapy
Cetuximab Reconstruction Results
Original publication Reconstructed KM data
Radiotherapy arm
2-year survival (%) 55 55 (49, 63)
3-year survival (%) 45 45 (39, 52)
median survival (months) 29.3 29.6 (22.6,43.0)
Radiotherapy plus cetuximab arm
survival rate (2 years) 62 62 (55, 69)
survival rate (3 years) 55 55 (48, 62)
median duration 49.0 48.9 (34.2, NA)
Hazard ratio with 95%CI
0.74 (0.57, 0.97) 0.77 (0.59, 0.996)
Back to Extrapolation ...
• Using reconstructed data we can estimate a variety of different survival models ...
Exponential Extrapolation (poor fit)
0 5 10 15 20 25 30 35 400
10
20
30
40
50
60
70
80
90
100
Years
ove
rall
su
rviv
al (
%)
Mean survival difference: 17mths (2.1, 33.45)
Weibull Extrapolation (poor fit)
0 5 10 15 20 25 30 35 400
10
20
30
40
50
60
70
80
90
100
Years
ove
rall
su
rviv
al (
%)
Mean survival difference: 23.3mths (0.7, 54.5)
Log-Normal Extrapolation (good fit)
0 5 10 15 20 25 30 35 400
10
20
30
40
50
60
70
80
90
100
Years
ove
rall
su
rviv
al (
%)
Mean survival difference: 80.4mths (2.0, 237.0)
0 5 10 15 20 25 30 35 400
10
20
30
40
50
60
70
80
90
100
Years
ove
rall
su
rviv
al (
%)
Assessing Fit to Trial Data Doesn’t Help
Mean Survival Difference:Log-normal FSEA: 80.4months (2.0,237.0); DIC=2314Log-normal AFT: 32.3months (-3.1,78.6); DIC=2315
Possible Solution: Use External Data To Inform Extrapolation
• Observational evidence e.g.• General population• Registry (e.g. Surveillance Epidemiology and End
Results)• Other RCT evidence e.g.
• Meta-analyses (e.g. Pignon et al. 2009)• longer RCTs
• Expert opinion
Estimation• Model RCT and external data
simultaneously with linked parameters• Bayesian approach• Eg constraint that general population
overall survival better than that in Bonner control arm
• Linking function:• Prior:
0,( ) ( )GP RCTS T S T )1,0(~Uniform
0 5 10 15 20 25 30 35 400
10
20
30
40
50
60
70
80
90
100
Years
ove
rall
su
rviv
al (
%)
Kaplan-MeierLog-Normal ExtrapolationMatched General Population
Matched General Population (Expect OS better than Bonner Control Arm)
• Rules out all parametric models• We used flexible spline models
(Royston & Parmar (2002))
Expert view on Bonner trial• In H&N, relapse is high for first 2 years, and then
declines• Effect of cetuximab is to increase the proportion of cells
sensitive to radiotherapy and so lower the risk of relapse
• Duration of treatment effect should be the same as the time interval over which the relapses occur
• Those who die of H&N cancer tend to die in first 5 years
• Conditional survival in both arms should “stabilize” and converge after 5 years (i.e. HR tends to 1)
Data: SEER 1-yr Conditional Survival
0 5 10 15 20 2540
50
60
70
80
90
100
matched SEER population with Bonner trial character-istics
radiotherapy from Bonner trial
radiotherapy plus cetuximab from Bonner trial
Years after the start of the RCT
1-ye
ar c
on
dit
ion
al s
urv
ival
(%
)
Data: Pignon meta-analysis 1-yr Conditional Survival
0 1 2 3 4 5 6 7 8 9 1050
55
60
65
70
75
80
85
90
95
100
radiotherapy from Pignon meta-analysissurgery +/- radiotherapy from Pignon meta-analysisradiotherapy from Bonner trialradiotherapy plus cetuximab from Bonner trial
Years after the start of the RCT
1-y
ea
r c
on
dit
ion
al
su
rviv
al
(%)
All Constraints
• Control arm overall survival less than matched UK general population
• 1-year conditional survival in control arm is no different to that in SEER database
• Hazard ratio tends to 1 as time from treatment increases
Implementation: Gen Pop Survival
• Likelihood for the external data: r: number alive at time T; n: number at risk at time 0
• Linking function Overall survival , e.g.
• Prior: Constrain general population survival to be better than that for
advanced head and neck cancer patients
0,( ) ( )GP RCTS T S T
)1,0(~Uniform
years 40T
~ ( ( ), )GPr Bin S T n
Implementation: SEER 1-year Conditional Survival
• Belief that 1-year conditional survival on radiotherapy equal to that from SEER
• Linking functions Binomial likelihood (each time-point conditionally independent) 1-year Conditional survival on control arm
0,( | 1) ( | 1)SEER S S RCT S SCS t t CS t t
years 26,...,7,6st
Implementation: HR tends to 1
• Belief that HR tends to 1• Likelihood for the external data
Normal for external HR
• Linking functions Normal likelihood for hazard ratios: Hazard ratio of treatment vs. control
1, 0,( ) ( ) / ( )EXT EXT RCT EXT RCT EXTt h t h t
years 35,34,26,25,..,7 ,6EXTt
2( ) ~ ( ( ), ( ))EXT EXT EXT EXT EXT EXTHR t N t t
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.00
10
20
30
40
50
60
70
80
90
100
Ove
rall
Su
rviv
al (
100%
)
Years
Results: Overall SurvivalKaplan-MeierMatched General PopulationConstrained Extrapolation
1-year Conditional Survival
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.00
10
20
30
40
50
60
70
80
90
100
1-ye
ar c
on
dit
ion
al s
urv
ival
(%
)
Years
Kaplan-MeierMatched SEERConstrained Extrapolation
Hazard Ratio
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.00.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
constrained spline ExpertKM HR
Haz
ard
Rat
io
Years
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.00
10
20
30
40
50
60
70
80
90
100
Kaplan-Meier radiotherapy armconstrained spline radio-therapy arm General population
Ove
rall
Su
rviv
al (
100%
)
Years
Overall SurvivalDifference in life expectancy: 5 months [95%CrL: 0; 9]
Discussion
• Spline models tricky to estimate• Possible alternative flexible models
include fractional polynomials, mixture models
• Relies on identification of relevant external evidence sources
• Clinical input essential to help identify relevant sources
References
• Bonner et al. 2006. NEJM 354: 567-78• Pignon JP et al. 2009. Radiotherapy and Oncology 92:4-14• Surveillance, Epidemiology, and End Results (SEER)
Database (www.seer.cancer.gov)• Guyot P, Welton NJ, Ades AE. Enhanced secondary analysis
of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Medical Research Methodology 2012. 12:9
• Royston P, Parmar MK. 2002. Stats in Med 21:2175-2197