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Multi-State Simulation Modelling with Short-Term Single Arm Clinical Trial
Data: The Case of CAR T-Cell Therapy
Petros Pechlivanoglou, PhD
The Hospital for Sick Children, University of Toronto
CADTH Symposium 2019
16 - 4 - 2019
Acknowledgments
Research Team Jill Furzer PhD(c) Sumit Gupta MD PhD Jason Pole PhD Paul Nathan MD Tal Schechter MD
Funding Disclosure: This project was supported by the Pediatric Oncology Group of Ontario (POGO) Seed Grant Fund Conflict of Interest None
Regenerative medicine & HTA
• Potential breakthrough in cancer research
• Promise for offering cure
• Anticipation & excitement around clinical use
• Push for early regulatory approval and use / reimbursement
• Challenging evaluation from regulatory authorities
Chimeric Antigen Receptor (CAR) T-cell Therapy
© Novartis
Effectiveness of CAR-T, 2014
Effectiveness of CAR-T, 2018
Background – CAR T-cell therapy
• The high cost of CAR T-cell (~ $600,000) caused significant debate around costly novel treatments. (e.g. CBC, healthy debate)
• The upfront large treatment cost must be offset by
sustained survival effects for CAR-T to be a good value for
money
Standard of Care in multi-relapsed ALL
• Current SOC multiple relapsed ALL: • Hematopoietic stem-cell transplant (HSCT)
• most effective but not all patients eligible/able to receive
• Alternative: salvage chemotherapy or palliative
• Survival in relapsed ALL is low: • 3-year Survival rate: 23% (Crotta et al., 2018) • Survival rate reduced with every additional relapse
POGO evaluation of CAR-T therapy in hard-to-treat ALL
Objectives:
For multi-relapsed CAR T-cell eligible pediatric ALL patients: 1. Estimate long-term survival for CAR T-cell and standard care
(SOC) 2. Evaluate the cost-utility (CU) of CAR T-cell therapy vs SOC
Challenges Ahead
CAR T-cell therapy Evidence Base 1. Multiple, single-arm studies – different published cut offs 2. Expert’s anticipation of “cure” 3. Short follow-up times (max of 3 years) 4. Censoring after trial follow up/ event 5. No access to patient-level data 6. Reporting on “modified” intention to treat 7. Generalizability (given trial design) 8. Propagating uncertainty SOC 9. Limited information on survival/ costs under SOC
Challenges Ahead
CAR T-cell therapy Evidence Base 1. Multiple, single-arm studies – different published cut offs 2. Expert’s anticipation of “cure” 3. Short follow-up times (max of 3 years) 4. Censoring after trial follow up/ event 5. No access to patient-level data 6. Reporting on “modified” intention to treat 7. Generalizability (given trial design) 8. Propagating uncertainty SOC 9. Limited information on survival/ costs under SOC
Data Sources
• Extract time to HSCT, survival after CAR T using digitized trials data (B2202, B2205J, B2101) – create pseudo patient-level dataset
B2101J
Novartis: Tisagenlecleucel (CTL019) for the
treatment of pediatric and young adult
patients
with relapsed/refractory B-Cell acute
lymphoblastic leukemia https://www.fda.gov/downloads/Advisory Committees/CommitteesMeetingMaterials/Drugs/ OncologicDrugsAdvisoryCommittee/UCM566168. pdf
Challenges Ahead
CAR T-cell therapy Evidence Base 1. Multiple, single-arm studies – different published cut offs 2. Expert’s anticipation of “cure” 3. Short follow-up times (max of 3 years) 4. Censoring after trial follow up/ event 5. No access to patient-level data 6. Reporting on “modified” intention to treat 7. Generalizability (given trial design) 8. Propagating uncertainty SOC 9. Limited information on survival/ costs under SOC
Defining effectiveness in SOC / post HSCT
“Matching” Patient level POGONIS registry data on ALL patients (Jan 1985 – Aug 2017) Match all >2 times-relapsed B-cell ALL patients diagnosed in Ontario Use eligibility criteria to enroll in a CAR T-cell trial
Age at diagnosis: Older than 2 and less than 21 (Median 10 years old) Male/Female ratio: 66% / 34%
Definition includes chemo, palliative and explicitly models HSCT Extract survival post 2nd relapse , time to HSCT, survival post-HSCT
Variable POGO Registry Data CAR T-Cell
Global Clinical Trial
Phase I/IIA Study
Pediatric Cohort
Age at treatment (Median/ range) 10 (3 to 20) 11 (3 to 23) 11 (5 to 22) Male (%) 0.66 0.43 0.56
Prior HSCT (%/range) 0.36 (0 to 1) 0.46 0.72 N Relapses (N ≥ 2) 2.19 (2 to 5) - 0.88
N 118 75 25 Global clinical trial results from Maude et al., 2018
Patient Demographic Comparisons: POGONIS to CAR-T trials
Challenges Ahead
CAR T-cell therapy Evidence Base 1. Multiple, single-arm studies – different published cut offs 2. Expert’s anticipation of “cure” 3. Short follow-up times (max of 3 years) 4. Censoring after trial follow up/ event 5. No access to patient-level data 6. Reporting on “modified” intention to treat 7. Generalizability (given trial design) 8. Propagating uncertainty SOC 9. Limited information on survival/ costs under SOC
Treatment State:
BMTRelapse State
Death State
GVHD
Figure 1: Standard of Care Multistate Model Arm
Death State
Treatment State
CAR T-cell
Adverse
Events
Figure 2: CAR T-cell Multistate Model Arm
Cure State
Relapse State
Treatment State:
BMT
GVHD
Treatment State:
BMTRelapse State
Death State
GVHD
Figure 1: Standard of Care Multistate Model Arm
Death State
Treatment State
CAR T-cell
Adverse
Events
Figure 2: CAR T-cell Multistate Model Arm
Cure State
Relapse State
Treatment State:
BMT
GVHD
• Non-relapse death informed by age- and sex-specific Canadian mortality data (2011) adjusted for ALL survival
Assumption of possible cure over 0 to 40% range
Methods: Multistate Modelling
Incorporates competing risks
Parametric survival analysis fitted for each transition
Death
CAR T-cell
Therapy
Adverse
Event
Cure
Relapse
2nd or more
HSCT
Treatment
GVHD
HSCT
TreatmentRelapse
2nd or more
Death
GVHD
Multistate model (msm)
• Fitting one parametric survival analysis for each transition in MSM
• For each survival model, if the event of interest is not experienced, the patient at risk is assumed censored at last observation
• Different distribution assumptions made for all survival models (exponential, Weibull, Gompertz, lognormal, splines)
• Goodness-of-fit and visual inspection used for defining best fit
Methods: Microsimulation
• Simulates disease progression/health outcomes for individual over lifetime using microsimulation model
• Repeat for 100,000 individuals to estimate expected value, standard deviation of health outcomes over a large population
20
Donna:
Julia: 2R 2R 2R 2R D D D D D D
2R 2R HSCT HSCT HSCT HSCT D HSCT HSCT HSCT
Outcomes • Quality adjusted life years (QALYs) • Healthcare payer costs • Incremental Cost-Utility Ratio
Scenario and probabilistic sensitivity analysis test uncertainty around input parameters
Bob: 2R 2R D D HSCT
t
HSCT HSCT HSCT HSCT HSCT
Results
Estimating Survival
Probabilistic analysis
Discussion
Discussion
•Use of external information, digitization and matching possible when addressing lack of data access
•Multistate models a better way to extrapolate survival outcomes and incorporate cure
•Microsimulation can facilitate reconstruction of ITT analysis
•Probabilistic analysis completely feasible (easier in R)
Limitations & next steps
• Cohort matching limited by characteristics in RCTs
• Immature data makes CE conclusions challenging
• Longer term data will inform more precise estimates
• Uncertainty around private manufacturer cost and SOC costs
• Use of mixture cure rate modeling (digitized data a challenge?)
Mixture cure rate models
29
Pre-progres
sion
Post-progres
sion
Disease- related death
Other-cause Death
Cure
π π: cure fraction in sample p*(t): all-cause mortality from Life-tables Once the fraction is estimated it can be incorporated to a Microsimulation/ Markov model
p*(t)
References • American Cancer Society. Cancer Facts and Figures 2014. Atlanta; 2014. https://www.cancer.org/content/dam/cancer-org/research/cancer-
facts-and-statistics/annual-cancer-facts-and-figures/2014/cancer-facts-and-figures-2014.pdf. Accessed June 21, 2018.
• Canadian Cancer Statistics Advisory Committee. Canadian Cancer Statistics 2018. Toronto, ON: Canadian Cancer Society; 2018. Available at: cancer.ca/Canadian-Cancer-Statistics-2018-EN. Accessed December 3 2018
• Bach PB, Giralt SA, Saltz LB. FDA Approval of Tisagenlecleucel. JAMA. 2017;318(19):1861.
• Cools J. Improvements in the survival of children and adolescents with acute lymphoblastic leukemia. Haematologica. 2012;97(5):635.
• Crotta A, Zhang J, Keir C. Survival after stem-cell transplant in pediatric and young-adult patients with relapsed and refractory B-cell acute lymphoblastic leukemia. Curr Med Res Opin. 2018;34(3):435-440.
• Hunger SP, Lu X, Devidas M, et al. Improved survival for children and adolescents with acute lymphoblastic leukemia between 1990 and 2005: a report from the children’s oncology group. J Clin Oncol. 2012;30(14):1663-1669.
• Lee DW, Kochenderfer JN, Stetler-Stevenson M, et al. T cells expressing CD19 chimeric antigen receptors for acute lymphoblastic leukaemia in children and young adults: a phase 1 dose-escalation trial. www.thelancet.com. 2015;385.
• Lin JK, Lerman BJ, Barnes JI, et al. Cost Effectiveness of Chimeric Antigen Receptor T-Cell Therapy in Relapsed or Refractory Pediatric B-Cell Acute Lymphoblastic Leukemia. J Clin Oncol. 2018
• Lin Y-F, Lairson DR, Chan W, et al. The Costs and Cost-Effectiveness of Allogeneic Peripheral Blood Stem Cell Transplantation versus Bone Marrow Transplantation in Pediatric Patients with Acute Leukemia. Biol Blood Marrow Transplant. 2010;16:1272-1281.
• Maude SL, Frey N, Shaw PA, et al. Chimeric Antigen Receptor T Cells for Sustained Remissions in Leukemia. N Engl J Med. 2014;371(16):1507-1517.
• Maude SL, Laetsch TW, Buechner J, et al. Tisagenlecleucel in Children and Young Adults with B-Cell Lymphoblastic Leukemia. N Engl J Med. 2018;378(5):439-448.
• Whittington, M. D., McQueen, R. B., Ollendorf, D. A., Kumar, V. M., Chapman, R. H., Tice, J. A., … Campbell, J. D. (2018). Long-term Survival and Value of Chimeric Antigen Receptor T-Cell Therapy for Pediatric Patients With Relapsed or Refractory Leukemia. JAMA Pediatrics.
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