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To adapt or to confirm: what is the question?

To adapt or to confirm: what is the question? . Vlad Dragalin Wyeth Research. Outline. Dose-ranging studies Adaptive model-based designs Statistical Operational Characteristics Conclusion. PhRMA ADRS Working Group. One of 10 PISC WGs formed 4 years ago Overall goal:

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To adapt or to confirm: what is the question?

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  1. To adapt or to confirm: what is the question? Vlad Dragalin Wyeth Research

  2. Outline • Dose-ranging studies • Adaptive model-based designs • Statistical Operational Characteristics • Conclusion V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  3. PhRMA ADRS Working Group • One of 10 PISC WGs formed 4 years ago • Overall goal: • investigate and develop designs and methods for efficientlearning about DR • more accurate and faster decision making on dose selection and improved labeling • How: • evaluate statistical operational characteristics of alternative designs and methods via comprehensive simulation studies • Focus: • adaptive and model-based • dose-ranging designs and methods V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  4. ADRS WG: Members Co-Chairs: José Pinheiro and Rick Sax Original Members: Björn Bornkamp, Frank Bretz, Alex Dmitrienko, Greg Enas, Brenda Gaydos, Chyi-Hung Hsu, Franz König, Michael Krams, Qing Liu, Beat Neuenschwander Tom Parke, Amit Roy, Frank Shen New Members: Zoran Antonijevic Vlad Dragalin Parvin Fardipour Marc Gastonguay Bill Gillespie Frank Miller Krishna Padmanabhan Inna Perevozskaya Nitin Patel Jonathan Smith V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  5. Dose-Ranging Studies • The overall goal of dose-ranging studies is to establish the existence, nature and extent of dose effect: • Detecting DR: evaluate if there is evidence of activity associated with the drug, represented by a change in clinical response resulting from a change in dose (PoC); • Identifying clinical relevance: if PoC is established, determine if a pre-defined clinically relevant response (compared to the placebo response) can be obtained within the observed dose range; • Selecting a target dose: when the previous goal is met, select the dose to be brought into the confirmatory phase, the so-called target dose; • Estimating the dose response: finally, estimate the dose-response profile within the observed dose range. V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  6. Simulation Study: Complete Summary V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  7. New Adaptive Designs • AMCP-Mod–Adaptive MCP-Mod approach combining multiple comparisons and modeling (Bornkamp, Bretz, Pinheiro) • DCoD– D-optimal followed by a c-optimal design based on sigmoid Emax model (Dragalin, Padmanabhan) • IntR– Bayesian design minimizing average variance of all LS-estimates for “interesting part” of dose-response curve (Miller) • MultObj – Multi-objective optimal design incorporating 2nd order moments and based on inverse quadratic model (Smith) • T-Stat – Dose-adaptive design based on t-statistics (Patel, Perevozskaya) V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  8. AMCP-Mod Design V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  9. Adaptive MCP-Mod Design • Extension to a response-adaptive version of the MCP-Mod methodology using • optimal design theory to allocate new cohorts of patients • posterior model probabilities and posterior parameter estimates to update initial guesses • AMCP-Mod Before Trial Start • Select the candidate models (two logistic and one beta models) • Select “best guesses” for , m = 1, . . . ,M • Choose prior model probabilities p(Mm) • Choose prior for V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  10. Adaptive MCP-Mod Design at IA V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  11. Adaptive MCP-Mod Design at Trial End • Calculate optimal contrasts and critical value using MCP • Select one of the significant models for dose-response and MED estimation • Fit dose-response model and estimate MED • Bayesian model is only used for updating the design; the classical MCP-Mod procedure is wrapped around this. V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  12. DCoD: Adaptive Dc-optimal Design • Working Model Sigmoid Emax model (4 parameter logistic) Dragalin et al V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  13. Sigmoid Emax Fit V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  14. D- and c-optimal Designs • D-optimal design (LDoD) minimizes • c-optimal (LcoD) minimizes • Kiefer-Wolfowitz Equivalence Theorem V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  15. Sigmoid Emax Scenario V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  16. Emax Scenario V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  17. Emax Low Scenario V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  18. Umbrella Scenario V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  19. Adaptive Dc-optimal Design • For 2 adaptations: • 1/3rd of the subjects allocated according to a fixed 5-dose design • Parameters are estimated –> next 1/3rd allocated according to augmented LDoD • Parameters are re-estimated –> final 1/3rd allocated according to augmented LcoD V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  20. IntR Design • Estimation of the interesting part of the dose-response curve • Working model: sigmoid Emax • Inference based on LS estimates from this Emax-sigmoid model • Minimize average variance of all LS-estimates for f(x) - f(0) with xδ<x<8. xδ is dose with effect 1 compared to placebo • “Detecting Dose-Response”: trend test used to test null hypothesis of flat dose-response xδ V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  21. IntR Design V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  22. MULTOBJ Design • Primary focus within MULTOBJ criterion is MED estimation • Lower weighted components also included related to POC and EDp (for a range of p’s) • Additional low wt. components related to nonlinearity • Weights chosen to reflect importance of component criteria • All of above based on 2nd order moments V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  23. MULTOBJ Design • MULTOBJcriterion is essentially an extended form of S-optimality but incorporating 2nd order moments and with MSE in place of variances • Working Model: Non-Monotonic 4 parameter Inverse Quadratic model V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  24. T-statistic design • Non-parametric design adaptive approach • Concentrates dose allocations around the dose with target (pbo-adjusted) response level • Patients are randomized sequentially in cohorts of fixed size; all assigned to the same dose or pbo (e.g. 3:1) • Dose selection is adaptive and driven by the value of t-statistic at the last dose studied (Ti): • Escalate to xi+1 if Ti • Stay at xi if -<Ti≤ • De-escalate to xi-1 if Ti ≤- • Where x1=pbo, x2,…, xK are active doses, • xiis current dose (at the time of IA), • Tiis standardized pbo-adjusted mean response at dose xi •  is a design parameter Ivanova A., Bolognese JA, Perevozskaya I. Adaptive dose-finding based on t-statistic in dose-response trial. Stat in Medicine, 2008; 27:1581-1592 V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  25. Simulation Study: Assumptions • Doses: • 9 doses: {0,1,2,3,4,5,6,7,8} • 5 doses: {0,2,4,6,8} • Endpoint: change from baseline in VAS score • Clinically meaningful difference: –1.3 • Variance: 4.5 • Sample Size: 250 • Number of adaptations: 0,1,2,4,9 • Total of 56 combinations V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  26. Simulation Scenarios • Linear: y = -(1.65/8) d +  • Umbrella: y = -(1.65/3)d + (1.65/36)d2 +  • Sigmoid Emax: y = -1.70 d5/(45 + d5) +  • Emax: y = -1.81 d/(0.79 + d) +  • Emax low: y = -1.14 d/(0.79 + d) +  • Explicit: y = {0, -1.29, -1.35, -1.42, -1.5, -1.6, -1.63, -1.65, -1.65} +  • Flat: y = . V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  27. Simulation Scenarios V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  28. Performance metrics • Probability of detecting dose response: Pr(DR) • Probability of identifying clinically relevant dose: Pr(dose) • Target Dose selection • Distribution of selected doses • Summary statistics (mean and standard deviation) for percentage difference from target • pDiff = 100( d - dtarg)/dtarg • Dose Response estimation: • summary statistics for average prediction error • (PE): 100|DRest - DR|/DR • Subject Allocation pattern V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  29. Detecting Dose-Response: type I error V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  30. Detecting D-R: power for 9 doses V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  31. Identifying clinically-relevant dose: flat D-R V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  32. Identifying clinically-relevant dose V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  33. Selecting Target Dose:9 doses V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  34. Selecting a target dose: distribution V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  35. Estimating D-R curve V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  36. Summary and Conclusions • Detecting DR is considerably easier than estimating it • Current sample sizes used for D-R studies are inadequate for dose selection and D-R estimation • Adaptive methods lead to gain in power to detect DR + precision of target dose selection + DR estimation compared to traditional ANOVA design V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  37. Summary and Conclusions (cont.) • None of the designs was a uniformly superior to the others • All 5 designs performed well with respect to achieving specific objective they were designed for: • IntR, did well for DR estimation • AMCPMod, t-test: did well for dose selection • MULTOB and DcoD: did well for both objectives • The appeal of a particular design will depend on • the specific goal of the trial • and the set of plausible DR scenarios, because the latter affects relative performance of the designs V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

  38. Summary and Conclusions (cont.) • Due to complexity of the designs, operating characteristics can be assessed only via simulations during the DR trial planning stage • Need software which is sufficiently flexible, comprehensive and extensible to allow in-depth exploration of various methods to determine design most appropriate for the study • We investigated impact of only one component of AD: allocation rule and adaptation based on efficacy endpoint only • The approach can be extended to examine other sources of “adaptivity”: • sampling rule, • early stopping for futility/efficacy, • information-driven SS determination, • using early data through longitudinal modeling V. Dragalin | To adapt or to confirm: what is the question?| St. Petersburg, Russia

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