Real World Data for Oncology Drug Development: Promise and Pitfalls Joint Statistical Meeting 2019 THURSDAY, August 01, 2019 CATHY Tuglus PRINCIPAL BIOSTATISTICAN, Amgen Public Information

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 Public Information

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 Public Information

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 Public Information

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 Public Information

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. Public Information

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 Public Information

Blincyto® (Blinatumomab) Case Study Adult R/R B-cell Leukemia Public Information

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 Public Information

BLINCYTO TRIALS Adult R/R Single Arm (J. Clin Onc. 32, no. 15_suppl (May 20 2014) 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 Public Information

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 Public Information

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 Public Information

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. Public Information

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 (0.39. 1.06) Public Information

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 Public Information

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 Public Information

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 Public Information

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. Public Information

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 Public Information

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 Public Information

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 Public Information