1. Real World Data for Oncology Drug Development: Promise and Pitfalls Joint Statistical Meeting 2019 THURSDAY, August 01, 2019
CATHY Tuglus
PRINCIPAL BIOSTATISTICAN, Amgen
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2. outline Introduction Single Arm studies in Oncology
Real-world data regulatory landscape in Oncology
Propensity Score analysis
Blinatumomab Case Study
Overview Adult R/R Leukemia Trials and Analyses
Comparison of results
What “if” scenario
Conclusions and Discussion
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3. Introduction Phase II single arm trials (SATs) are common in oncology
Rare, serious, life-threatening diseases
Standard of care can vary by region
Randomized trials difficult to enroll
Regulatory pathways allow for early approval
However, approval in some regions extremely challenging without randomized data
Increasing use of real world data to evaluate SATs
Various methods for such evaluations
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4. SAT’s In Oncology Pros:
Guaranteed treatment access to seriously ill patients
Interim analyses allow for early stopping for futility or toxicity
Less reluctance to participate by investigators
Cons:
No comparison group
Limited interpretation relative to the literature, other clinical trials, or real world expectations
Trial population may be less fit than a randomized population
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5. Real World Data (RWD) in Regulatory decision making
Guidance documents are opening the door to the use of RWD in drug development
General principles are provided
Specific analysis methods are not dictated
U.S.A: 21st Century Cures Act (2016)
Wider use of novel trial designs and real world evidence
Specifies use of Real World Evidence for new indications of already- approved drugs.
Framework for FDA’s Real World Evidence Program (2018): When “control arm is unethical or not feasible”
Blinatumomab is cited as an example
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6. Use of RWD For Regulatory Decision making: Propensity Score Analyses
Propensity score methods are being a common tool for using real world data to interpret clinical trial results.
Align the treatment arms based on a set of prognostic variables
Weighting and Matching based on propensity to be treated
Attempt to mimic a randomized trial to estimate the causal relationship
Improvement over comparisons to historical benchmarks, provides method to control for confounding
Limited based on availability of important prognostic covariates in both populations, and the consistently of SOC over the relevant time period.
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7. What are Propensity Scores?
The propensity, p, to receive a treatment (T=1) conditional on observed covariates, X
p(X) = Pr(T=1 | X)
Non-treated groups may have a different propensity to be treated than a treated group (i.e. due to different risk factors).
Comparisons between the two groups are therefore not fair.
Propensity score (PS) analyses can make comparisons fair
They can mimic the effects of randomization
Reduces differences due to a variety of factors to a single scalar
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8. Blincyto® (Blinatumomab) Case Study
Adult R/R B-cell Leukemia
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9. EU/EMA approval relied on a requested propensity score analysis
US/FDA approval relied partially on patient-level comparisons to historical data
EU/EMA approval relied on a requested propensity score analysis
Source: K Sprugel, D Nagorsen; 2015 AAADV Conference
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10. BLINCYTO TRIALS
Adult R/R Single Arm (J. Clin Onc. 32, no. 15_suppl (May ) 7005.)
Multinational single-arm (N=189)
Objective: Show complete response (CR) and CR with partial hematologic recovery was >30%
Historical comparator study (Blood Cancer Journal volume 6, page e473 (2016))
Provides context for SA (N=765)
Weighted analysis conducted, 6 strata based on weight factors used to justify “30%”
Tower Adult R/R Randomized Trial (N Engl J Med 2017;376:836-47)
open-label, multi-center, phase 3 randomized clinical trial with nearly identical entry criteria as the single-arm study
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11. Propensity SCORE ANALYSIS
Estimate Propensity to be treated
Common baseline covariates and all two way interactions are selected using stepwise model selection in a logistic regression model
Inverse Probability of treatment weights
Compute weights based on estimated propensity to be treated
Assess Covariate balance between the two populations
Apply to Cox proportional hazards regression model to compare the treatment groups
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12. BASELINE CHARACTERISTICS and BALANCE
Propensity score analysis achieves adequate balance
Randomized trial has better balance (as expected)
TOWER Randomized Trial
Single Arm vs Historical Control (unadjusted)
Single Arm vs Historical Control (adjusted)
Blin
Control
Std Diff
Age
40.8
41.1
-0.016
41.4
37.7
0.239
36.2
38.4
-0.147
Duration from diagnosis to recent salvage
26.4
28.6
-0.061
23.9
11.3
0.697
16.8
14.4
-0.14
Line of salvage
1.9
1.78
0.125
2.34
1.45
1.016
1.65
1.64
0.008
Sex (male)
0.60
0.58
0.047
0.63
0.56
-0.141
0.62
-0.125
Region (US)
0.11
0.5
0.17
-0.764
0.23
0.009
Primary refractory
0.10
0.13
-0.12
0.02
0.06
-0.201
0.05
0.201
Last refractory
0.20
-0.082
0.52
0.21
0.703
0.25
0.27
-0.038
Prior HSCT
0.35
0.34
0.289
-0.065
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13. Comparison of Propensity Score Analysis Population to The randomized Population
Distribution of Propensity Scores by Treatment Group and Study
sIPTW Distribution of Propensity Scores
Randomized data applied to the original PS model
As expected, randomized population is in between historical population with respect to propensity to be treated with blinatumomab in the single-arm trial.
However, after adjustments, PS analysis population is more similar to the original control group rather than the randomized group.
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14. Propensity Score Analysis*
Comparison of Propensity Score Analysis to The randomized Analysis: Overall Survival
Randomized Analysis
Propensity Score Analysis*
HR=0.71
HR=0.64
95% CI: (0.55, 0.93)
95% CI: (0.53, 0.79)
Randomized
blinatumomab
sIPTW blinatumomab
Randomized control
sIPTW control
* The original analysis applied the weights via the WEIGHT statement in PHREG along with robust sandwich variance estimation, which has a profound and inaccurate effect on the variance. Original CI were ( )
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15. How close were the PS analysis results to those from the randomized study?
Quite close
Differences possibly due to slight differences in the patient populations (randomized vs. PS analysis) following the propensity score adjustments
Historical controls possibly did slightly worse compared to contemporaneous controls due to treatment improvements over time
Improved chemotherapy regimens
Improved stem-cell transplants, availability of donors
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16. Comparing SA And Randomized TREATMENT ARM
Single Arm population was a sicker population than randomized treatment arm
Tower was completed primarily outside the US
Blinatumomab
TOWER
Single Arm
Std Diff
Age
40.8
41.4
0.018
Duration from diagnosis to recent salvage
26.4
23.9
0.049
Line of salvage
1.9
2.34
0.476
Sex (male)
0.60
0.63
0.065
Region (US)
0.11
0.5
0.914
Primary refractory
0.10
0.02
-0.322
Last refractory
0.17
0.52
0.789
Prior HSCT
0.35
0.34
-0.017
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17. Comparing Historical Control And Randomized Control
Control
TOWER
Single Arm
Std Diff
Age
41.1
37.7
0.231
Duration from diagnosis to recent salvage
28.6
11.3
0.596
Line of salvage
1.78
1.45
0.299
Sex (male)
0.58
0.56
-0.007
Region (US)
0.11
0.17
-0.418
Primary refractory
0.13
0.06
0.274
Last refractory
0.20
0.21
-0.067
Prior HSCT
0.34
0.365
Randomized Control and Historical Control exhibit differences in past treatment: Prior HSCT, time from diagnosis
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18. What if Adult R/R SA used the more “contemporary” CONTROL from TOWER
Randomized Analysis
Single Arm and Historical Control:
Propensity Score Analysis
Single Arm and Tower Control:
HR=0.71
HR=0.64
HR=0.62
95% CI: (0.55, 0.93)
95% CI: (0.53, 0.79)
95% CI: (0.47, 0.82)
Standardized Differences
Unadjusted
After Adjustment
Age
0.008
-0.041
Duration from diagnosis to recent salvage
-0.073
0.076
Line of salvage
0.589
-0.108
Sex (male)
0.11
-0.008
Region (US)
0.92
-0.067
Primary refractory
-0.43
Prior HSCT
-0.01
0.036
“Time-machine” example
Good balance is achieved. Single arm patients are sicker than tower control patients
HR is similar to original propensity score analysis
Differences between TOWER HR and original PS HR may be more to do with differences in treatment population than changes in care over time.
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19. Conclusions
In many oncology settings (rare, life threatening diseases), drug development is challenging:
Regulators want controlled data
Investigators want equipoise
Use of external controls to evaluate new therapies from SATs can be applied to oncology settings
This has been done with devices (FDA/CDRH) for over 10 years
Regulatory environment opening up to this more broadly (21st Century Cures Act)
Propensity score analysis provides additional evidence over comparisons with historical efficacy benchmarks
Attempts to control for confounding and balance treatment arms to mimic a randomized trial
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20. Conclusions Case-study: Blincyto If only we had a time-machine. . .
Rare situation where single arm trial and confirmatory randomized trial are in same population
PS survival estimates were worse than randomized trial
Differences in baseline characteristics
Improvement in management of R/R over time
If only we had a time-machine. . .
Prospectively collected RWD can address time bias, however this is difficult in Oncology setting
PS analysis with Tower Control showed similar hazard ratio to original PS analysis
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21. ACKNOWLEDGMENTS Co-authors and Collaborators
Chris Holland (Executive Director, Head of Biostatistics, Immunocore)
Qui Tran (Biostatistics Manager, Amgen)
Thanks to Alex Fleishmen and all Blinatumomab team members who contributed to the three studies
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