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  • 1. Statistical Considerations for Immunologic Outcomes
  • 2. Design Issues
    • Early phase trial designs for vaccines / immunotherapies
    • - what are the considerations?
    • - what are the options?
  • 3.
    • Higher toxicity associated with higher efficacy.
    • Need to balance toxicity with efficacy.
    Premise in Phase I Trials
    • Shallower dose-toxicity curve
    • Non-monotonically increasing dose-efficacy curve
    • Dose selection driven by safety and biological effect
    • Proof-of-principle of biologic effect in early trials
    Dose-toxicity and efficacy curves Cytotoxic agents Dose-toxicity and efficacy curves Cytostatic/biologic agents
  • 4. Design Issues What are the options?
    • Algorithm-based designs
    • - 3+3 and other variants
    • - adapt to vaccine/immunotherapy trials
    • Model-based designs
    • - CRM and other variants
    • - application to T-cell immunotherapy
    • trial example
  • 5. Example Trial: TGF- β resistant CTLs in HD and NHL Dose levels and target toxicity Target toxicity rate: 0.20 Dose Group Dose Increase of Dosage (Modified Fibonacci) Prior Probability of Toxicity 1 Day 0 2 x 10 7 cells/m 2 D (2) 0.05 Day 14 2 x 10 7 cells/m 2 2 Day 0 6 x 10 7 cells/m 2 3xD (3.3) 0.08 Day 14 6 x 10 7 cells/m 2 3 Day 0 1.5 x 10 8 cells/m 2 7.5xD (5.0) 0.25 Day 14 1.5 x 10 8 cells/m 2
  • 6. Example CRM Simulations for Trial Average Number of Toxicity 1 1.5 2 2.5 3 Scenario 1 (0.05,0.08,0.25) Scenario 2 (0.05,0.08,0.21) Scenario 3 (0.05,0.15,0.30) Numbers 3+3 Exponential Logistic
  • 7. Example CRM Simulations for Trial Percent of Patients at Target Dose Level Target Toxicity = 0. 20 0 10 20 30 40 50 60 70 80 90 100 Scenario 1 (0.05,0.08,0.25) Scenario 2 (0.05,0.08,0.21) Scenario 3 (0.05,0.15,0.30) Percent 3+3 Exponential Logistic
  • 8. Example CRM Simulations for Trial
  • 9. Early Phase trials Design Issues
    • Do we design for toxicity and immunologic/ biological effect?
    • - what are the options?
    • (e.g. Thall/Russell, Braun, TriCRM)
    • - which immune outcome to use?
    • - how is efficacy/immune response defined?
    • mean + 3SD of reference popn (based on LDA for positive SI cut-off)
    • >2-fold increase post-immunization (minus control values) and minimum of
    • 10 Ag-specific ELISPOTS
    • mean +2SD or +3SD of baseline values below LLD
  • 10. Analysis of Immune Outcome Data
    • Examples and Methods
  • 11. Analysis of Immune Outcome Data
    • Generate summaries, graphs
    • Assess missing data points
    • Paired tests for changes (baseline, over time)
    • Immune response rates
    • Summary statistics of immune response kinetics
    • (AUC, longitudinal models)
    • Multiple immune outcome data
    • Correlations
  • 12. Design and Validation of Surrogate Biomarkers in Clinical Trials
    • • Issues
    • - embedded within clinical trials
    • - small subset of patients
    • - sample heterogeneity
    • - biomarker prevalence
    • - testing of multiple biomarkers
    • - correlation with clinical endpoint
  • 13. Design and Validation of Surrogate Biomarkers in Clinical Trials
    • • Designs
    • - parallel design by biomarker status
    • - marker by treatment interaction
    • - marker-based strategy
    • - modified marker-based strategy
    • • Sample size requirements by design
  • 14. Correlation of Immune Outcomes and Biomarkers with Clinical Outcomes
    • Examples and Analysis strategies
  • 15. References
    • Greene S, Benedetti J, Crowley J. Clinical Trials in Oncology, 2nd ed. Florida, Chapman and Hall CRC. 2003.
    • Chevret S. Statistical Methods for Dose-Finding Experiments, John-Wiley and Sons, 2006.
    • Goodman SN, Zahurak ML, Piantadosi S. Some practical improvements in the continual reassessment method for phase I studies. Stat Med. 14(11):1149-61, 1995.
    • Thall PF, Russell KE. A strategy for dose-finding and safety monitoring based on efficacy and adverse outcomes in phase I/II clinical trials. Biometrics. 54:251-264, 1998.
    • Zhang W, Sargent DJ, Mandrekar S. An adaptive dose-finding design incorporating both toxicity and efficacy. Statistics in Medicine online. Oct. 2005.
    • Diggle PJ, Liang, KY, Zeger SL. Analysis of Longitudinal Data. New York, Oxford University Press, 1994.
    • Walker EB, Disis ML. Monitoring immune responses in cancer patients receiving tumor vaccines. Intern. Rev. Immunol. 22:283-319, 2003.
    • Salazar LG, Fikes J, Southwood S, Ishiokka G, Knutson KL, Gooley TA, Shciffman K, Disis ML. Immunization of cancer patients with HER-2/neu-derived peptides demonstrating high-affinity binding to multiple class II alleles. Clin. Cancer Research. 9:5559-5565, 2003.
    • Caliguiri MA, Velardi A, Scheinberg DA, Borrello IM. Immunotherapeutic approaches for hematologic malignancies. Hematology. 337-353, 2004.
    • Eisenbeis CF, Grainger A, Fischer B, Baiocchi RA, Carrodegaus L, Roychowdhury S, Chen L, Banks AL, Davis T, Young D, Kelbick N, Sthephens J, Byrd JC, Grever MR, Caliguiri MA, Porcu P. Combination immunotherapy of B-cell non-Hodgkin’s lymphoma with rituximab and interleukin-2: A preclinical and phase I study. Clin. Cancer Research. 10:6101-611-, 2004.
    • Hoos A, Parmiano G, Hege K, Sznol M, Loibner H, Eggermont A, Urba W, Blumenstein B, Sacks N, Keilholz U, Nichol G for CVCTWG. A clinical development paradigm for cancer vaccines and related biologics. J Immunotherapy. 30(1): 1-15, 2007.
    • Sargent DJ, Conley BA, Allegra C, Collette L. Clinical trial designs for predictive marker validation in cancer treatment trials. JCO. 23(9): 2020-2027, 2005.