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Introduction to Adaptive Designs: Definitions and Classification

Introduction to Adaptive Designs: Definitions and Classification. Recent DIJ Publications by PhRMA Working Group on Adaptive Designs ( Drug Information Journal, Vol. 40, 2006). P. Gallo, M. Krams Introduction V. Dragalin Adaptive Designs: Terminology and Classification

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Introduction to Adaptive Designs: Definitions and Classification

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  1. Introduction to Adaptive Designs: Definitions and Classification AD course for Philadelphia ASA Chapter

  2. Recent DIJ Publications by PhRMA Working Group on Adaptive Designs(Drug Information Journal, Vol. 40, 2006) • P. Gallo, M. Krams Introduction • V. Dragalin Adaptive Designs: Terminology and Classification • J. Quinlan, M. Krams Implementing Adaptive Designs: Logistical and Operational Considerations • P. Gallo. Confidentiality and trial integrity issues for adaptive designs • B.Gaydos, M. Krams, I. Perevozskaya, F.Bretz; Q. Liu, P. Gallo, D. Berry; C. Chuang-Stein, J. Pinheiro, A. Bedding.Adaptive Dose Response Studies • J. Maca, S. Bhattacharya, V. Dragalin, P. Gallo, M. Krams, Adaptive Seamless Phase II / III Designs – Background, Operational Aspects, and Examples • C. Chuang-Stein, K.Anderson, P. Gallo, S. Collins. Sample Size Re-estimation: A Review and Recommendations

  3. Outline • Adaptive design: evolution if the term • Adaptive vs. static designs • Some adaptive designs were known under different names • Formal classification effort: • Structure and key elements • Classification by Objective and Phase or Stage • Adaptive designs “ahead of others” (where effort should be focused) • dose response • seamless II/III • Sample size re-estimation

  4. Adaptive vs. Traditional Designs • In traditional drug development, most designs used (especially Phase II and III) are “static”: • Key elements driving the designs are specified in advance: • Hypotheses to be tested • Population of interest • Maximum information to be collected (translated into power, SS, and detectable treatment effect) • Randomization scheme • Early stopping rules

  5. Adaptive vs. Traditional Designs (cont.) • “Static” designs framework: • Results observed during trial are not used to guide it’s course • This setup provides solid inferential procedures • But leaves some space for improvement in terms of efficiency • Different ways to improve efficiency have been proposed over time, allowing dynamic modification of trial’s design during its course based on accumulating data • That lead to formation of a broad group of methods known today as “adaptive designs”

  6. Adaptive vs. Traditional Designs (cont.) Definition: (from An Executive Summary of PhRMA Working Group): Adaptive design refers to a clinical study design that uses accumulating data to decide on how to modify aspects of the study as it continues, without undermining the validity and integrity of the trial • Essential components: • changes are made by designs and not on an ad-hoc basis • adaptation is a design feature and not a remedy for poor planning

  7. Adaptive Designs: Evolution of the Term • Many of designs we call “adaptive” today existed for quite some time as a “class of their own” • (e.g. group-sequential designs, response-adaptive randomization, flexible designs, sample size re-estimation ) • These designs • Aim at improving some feature of a rigid traditional design (such as cost efficiency or addressing an ethical dilemma) • Share a common feature of mid-course adaptation(s) • As the number of such designs grew, so did the confusion… • Strong need for a unified structured approach to terminology has emerged

  8. Key Reference: V. Dragalin “Adaptive designs: Terminology and Classification“. Drug Information Journal (2006), Vol 40, pp 425-435 • First attempt to develop a unified approach to AD • Reflects discussions within PhRMA working group on adaptive designs • Major source of AD review to follow • Provides: • general definition of adaptive designs • structure (key components) • Classification (by objective) • mapping against drug-development process

  9. Review of “AD: Terminology and Classification”Adaptive Design Definition Adaptive design refers to a multistage clinical study design that uses accumulating data to decide on how to modify aspects of the study without undermining the validityand integrity of the trial • Validity: • Correct statistical inference • Ensuring consistency across different parts • Minimizing operational basis • Integrity: • Providing results convincing to the scientific community • Adequate pre-planning and blinding procedures

  10. Key Elements of anAdaptive Design • Allocation Rule • Sampling Rule • Stopping Rule • Decision Rule Examples: • Group sequential designs (stopping ) • Response-adaptive allocation (allocation) • Sample size reassessment (sampling) • Flexible designs (all) One or more may be applied during interim looks

  11. Key Elements of anAdaptive Design (cont.) 1. Allocation Rules: • Determine how patients are assigned to available treatments at each stage • Can be fixed (static) or adaptive (dynamic) • Fixed allocation examples: • Complete randomization • Stratified randomization • Restricted randomization • Adaptive allocation examples • Covariate-adaptive randomization • Response-adaptive randomization • Bayesian response-adaptive randomization (Berry, 2001) • Drop-the-loser type (Sampson, 2005) Rosenberger and Lachin, (2001)

  12. Key Elements of anAdaptive Design (cont.) 2. Sampling Rules • How many subject will be sampled at the next stage? • Examples of designs with SR: • Blinded SS re-estimation • Adjustment of SS based on estimate of a nuisance parameter • Unblinded SS re-estimation • Adjustment of SS based on information about trt effect • Traditional group sequential • fixed sampling rule • Flexible SSR based on conditional power • Probability of rejecting null at the end of study given first-stage data • Calculated for the originally specified treatment effect

  13. Key Elements of anAdaptive Design (cont.) 3. Stopping rules • Intended to protect patients from unsafe drug or to expedite the approval of a beneficial treatment. • Based on satisfying power requirements in hypothesis testing framework • “Crossing a boundary” methodology • Superiority • Harm • Futility • Examples: classical group-sequential (Jenisson&Turnbull, 2000)

  14. Key Elements of anAdaptive Design (cont.) 4. Decision rules: • Changing test statistics • Redesigning multiple endpoints • Selecting hypotheses to be tested or their hierarchy • Changing patient population • Choosing the number of interim analyses based on current information • For dose-response studies-selecting next dose assignment

  15. Classification of Adaptive Designs Ref: V. Dragalin “Adaptive designs: Terminology and Classification“. DIJ (2006) • Key elements of AD define structure and describe algorithms of AD • Allocation Rule • Sampling Rule • Stopping Rule • Decision Rule • Another way to classify AD is by • what their objectives are • applicability to a particular stage of clinical development

  16. Classification of Adaptive Designs (cont.) • Single-arm trials • Comparing two treatments • Comparing more than two treatments • Model-based dose-response assessment • Seamless Phase II/III

  17. 1. Adaptive Designs for Single-Arm Trials • Applicability: Phase-I/POC/Phase II • Screening trials for 1 trt-used to screen candidate components based on short-term response • Employ small sample sizes • Hypothesis testing: minimum acceptable probability of response pre-specified • Allow early stopping due to futility • Ex1: Two-stage designs (Gehan, 1961 ) • Ex2: Bayesian designs (Thall & Simon, 1994)

  18. 1. Adaptive Designs for Single-Arm Trials (cont.) • Designs for entire screening program • Minimize time to identify promising compound • Control Type I and Type II risk for the entire program • Ref: Wang&Leung, 1998; Yao&Venkatraman, 1998; Hardwick & Stout, 2002;

  19. 2. Adaptive Designs for Comparing Two Treatments • Applicability: predominantly Phase III, but some can be used in Phase I-II • Fully sequential design • Check boundary crossing after each patient • Group-sequential Design • Check boundary crossing after a group of patients • Adaptive group-sequential designs • Extend the GSD methodology: allow  in SS • Methodology based on P-value combination tests • Flexible designs • Wide spectrum of decision rules can be applied after 1st stage • Recursive application of 2-stage combination tests • Allow many mid-trial adaptations; not all prespecified (in theory….)

  20. 3. Adaptive Designs for Comparing More Than Two Treatments • Applicability: dose-response assessment studies (mostly phase II, full range I-III) • “Late stage dose-response development” • group-sequential designs (Stallard & Todd, 2003) • Flexible designs (Bauer & Kieser, 1999) • “Early exploratory development” • Dose-escalation studies (Phase I; Ex. CRM) • Model-based dose-response assessment • D-optimal designs • Bivariate response • Penalized (constrained) designs • Bayesian dose-finding designs • Reviewed in depth in (Gaydos et al., 2006)

  21. 4. Seamless Phase II/III designs • Combine traditional Phase IIb and Phase III • “learning and confirming” governed by one protocol • Can be • operationally seamless • inferentially seamless • Explored in depth in Maca et al., 2006

  22. Dose-Finding AD Example:Continual Reassessment Method (Ex.1) • Bayesian dose-escalation design • Designed to converge to MTD • For a predefined set of doses to be studied and a binary response, estimates dose level (MTD) that yields a particular proportion of responses • Updates MTD distribution after each patient’s response • Next dose is selected as the one with predicted probability closest to the target level of response • Procedure stops after N patients enrolled

  23. Continual Reassessment Method (cont.) Choose initial estimate of response distribution & choose initial dose Update Dose Response Model & estimate Prob. (Resp.) @ each dose Obtain next Patient’s Observation Next Pt. Dose = Dose w/ Prob. (Resp.) Closest to Target level Stop. MTD = Dose w/ Prob. (Resp.) Closest to Target level Max N Reached? no yes

  24. CRM Design example (1) • Post-anesthetic care patients received a single IV dose of 0.25, 0.50, 0.75, or 1.00 μg/kg nalmefene. • Response was Reversal of Analgesia (ROA) = increase in pain score of two or more integers above baseline on 0-10 NRS after nalmefene • Patients entered sequentially, starting with the lowest dose • The maximum tolerated dose = dose, among the four studied, with a final mean posterior probability of ROA closest to 0.20 (i.e., a 20% chance of causing reversal) • Modified continual reassessment method (iterative Bayesian proc) selected the dose for each successive pt. as that having a mean posterior probability of ROA closest to the preselected target 0.20. • 1-parameter logistic function for probability of ROA used to fit the data at each stage Dougherty,et al.ANESTHESIOLOGY (2000)

  25. CRM example (1) results * including the 1st patient treated (MTD), i.e., estimated mean posterior probability closest to 0.20 target ^ extrapolated

  26. CRM example (1) results Posterior ROA Probability (with 95% probability intervals) 1.0 0.8 0.6 0.4 0.2 0.0

  27. Continual Reassessment Method (cont.) • Allocation rule: model-based • Sampling rule: cohort size • Stopping rule: max N or no rule • Decision rule: posterior update, select next dose

  28. Example 2: Comparing 2 treatments Adaptive GS (Flexible) design Redesigned trial example from (Cui et al., 1999) • Actual design: group sequential design • Proposed design: sample size re-estimation + combination test statistic • Phase III trial for prevention of MI in patients undergoing coronary artery bypass graft surgery • N=600 per treatment group to detect 50% reduction of incidence (predicted 22% for placebo vs. 11% for drug) with 95% power • Interim analysis at 50% data: • N=300 per treatment group • Observed incidence for pbo was ~16.5%, drug~11% • Given observed data, power is 40% to detect 25% reduction

  29. Example 2 (cont.) • Sponsor wanted to increase 2nd stage sample size to detect smaller effect • Type I error rate would be inflated with usual group sequential test • Trial continued with planned sample size and ended with non-significant statistical result • Instead, authors proposed to  SS and use combination test • Simulations were performed: • Increase total sample size to 1400 per treatment group • Maintain Type I error rate; 93% power to detect 25% reduction

  30. Example 2 (cont.) • Allocation rule: fixed randomization • Sampling rule: sample size of next stage depends on results from previous stage • Stopping Rule: p-value combination test • Decision Rule: adapting alternative hypothesis and test statistics

  31. Summary: adaptive designs where attention needs to be focused • Dose-ranging studies: • B.Gaydos, M. Krams, I. Perevozskaya, F.Bretz; Q. Liu, P. Gallo, D. Berry; C. Chuang-Stein, J. Pinheiro, A. Bedding.Adaptive Dose Response Studies • Seamless Phase II/III • J. Maca, S. Bhattacharya, V. Dragalin, P. Gallo, M. Krams, Adaptive Seamless Phase II / III Designs – Background, Operational Aspects, and Examples • Sample Size Re-estimation • C. Chuang-Stein, K.Anderson, P. Gallo, S. Collins. Sample Size Re-estimation: A Review and Recommendations

  32. Conclusions • Adaptive designs provide an opportunity to redesign trials based on accumulating data • In some situations, may be more efficient than implementing traditional designs • There is no “ one-size-fits-all” recommendation for the choice of AD • In fact, it may not be the best solution at all • That decision will depend on: • Trials objectives • Regulatory guidelines • Logistic and practical consideration • Those are collectively determined by clinicians, regulatory, statisticians and data management => complicated process • As a result, implementation may be the biggest challenge • However, there are successful examples out there and that should be encouraging!!!

  33. Additional References • Rosenberger WF, Lachin JM. Randomization in Clinical Trials: Theory and Practice. New York: Wiley; 2002. • Berry D. Adaptive trials and Bayesian statistics in drug development. Biopharm Rep. 2001;9:1–11. • Sampson AR, Sill MW. Drop-the-Losers design: normal case. Biometrical J. 2005;47:257–268. • Cui L, Hung HMJ, Wang SJ. Modification of sample size in group sequential clinical trials. Biometrics. 1999;55:853–857 • Jennison C, Turnbull BW. Group Sequential Methods With Applications to Clinical Trials. Boca Raton, FL: Chapman and Hall; 2000. • Gehan EA. The determination of number of patients in a follow-up trial of a new chemotherapeutic agent. J Chronic Dis. 1961;13:346–353. • Wang YG, Leung DHY. An optimal design for screen trials. Biometrics. 1998;54:243–250. • Yao TJ, Venkatraman E. Optimal two-stage design for a series of pilot trials of new agents. Biometrics.

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