Adaptive Design Lecture Series 2008
From Biopharmaceutical Network
Presentations given in the adaptive design lecture series in 2008. The series was chaired by Frank Bretz and Judith Quinlan.
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January 11, 2008
Martin Posch (Medical University of Vienna)
Testing and estimation in adaptive group sequential designs with treatment selection
Integrating selection and confirmation phases into a single trial can expedite the development of new treatments. We consider adaptive group sequential trials to compare several treatments (or doses) with a control. In an interim analysis one or more of the considered treatments are selected to be continued in the further stages. The choice of treatments depends on the data accumulated so far. This includes typically not solely data on the primary endpoint but also data on secondary endpoints, safety data and also data from external sources as, e.g., other trials running in parallel. Adaptive designs based on combination tests are efficient tools to implement treatment selection without compromising the multiple type I error rate (Bauer and Kieser, 1999; Hommel, 2001). A crucial feature of these designs is that the treatment selection rule need not be specified in advance. For the sake of credibility, in clinical trials one should, however, specify the major options for treatment selection in the protocol. Besides hypotheses testing, we discuss the properties of point estimates and the construction of confidence intervals for designs with adaptive treatment selection (Posch et al. 2005).
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References
Bauer, P. and Köhne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics. 50, 1029-1041.
Müller, HH. and Schäfer, H. (2004). A general statistical principle for changing a design any time during the course of a trial. Statistics In Medicine. 23, 2497-2508.
Posch M., König F., Branson M., Brannath W. Dunger-Baldauf C. and Bauer P. (2005). Testing and estimation in flexible group sequential designs with adaptive treatment selection. Statistics in Medicine. 24, 3697-714.
Hommel, G. (2001). Adaptive modifications of hypotheses after an interim analysis. 43, 581-589
February 8, 2008
Tom Parke (Tessella)
Supply issues in adaptive clinical trials
Adaptive designs make drug supply during the trial much harder and if they feel they can't supply your trial you won't be able to run it. So what are the key issues for drug supply and how can we address them:
- There are more treatment arms, how do we supply more doses?
- Arms may be dropped/introduced or arms may become more/less likely to be allocated.
- How do we know how much of each dose we will need make/package?
- During the trial, how do we know which doses to ship?
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March 14, 2008
Stuart Pocock (London School of Hygiene and Tropical Medicine)
Adaptive designs for clinical trials: Insightfully innovative or irrelevantly impractical
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April 11, 2008
Nitin Patel (Cytel Inc)
Drug supply for adaptive trials
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June 13, 2008
Devan Mehrotra (Merck)
Interim analyses with multiple primary endpoints: Application to an HIV vaccine trial
Published research on group sequential methods and adaptive designs has largely focused on clinical trials with a single primary endpoint. In this presentation, we will discuss interim analysis strategies when there are two primary endpoints and the goal is to maximize the number of endpoints for which statistical significance can be demonstrated in a timely manner while ensuring control of the overall type I error rate. To illustrate the key ideas, we will describe an interim analysis strategy that was developed and implemented for a placebo-controlled HIV vaccine test-of-concept efficacy trial with dual primary endpoints: HIV infection status (infected/uninfected) and post-infection HIV RNA set-point (for those who become HIV infected). The proposed strategy can be extended to more general settings, including trials with more than two treatment arms and/or with than two primary endpoints.
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July 11, 2008
Cyrus Mehta (Cytel Inc)
Population enrichment within a group sequential design
We present a method for combining group sequential stopping rules with population enrichment within the framework of a confirmatory clinical trial. The approach is motivated by the need for greater efficiency in large cardiology trials where event rates are low and efficacy gains relative to the current standard of care are likely to be small. Since such trials typically involve sample sizes in the thousands, group sequential designs with early stopping for benefit or futility are commonly adopted. The chances of success with such trials can be greatly enhanced if patient eligibility is restricted to a subpopulation, selected from analysis of interim data that is sensitive to the new treatment. Statistical methodology for enriching an ongoing group sequential trial in this manner, possibly accompanied by a data dependent sample size increase, will be presented. The approach is general and can be applied to late stage clinical trials in other therapeutic areas besides cardiology.
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August 08, 2008
Tim Friede (University of Warwick)
Sample size recalculation in internal pilot study designs: A review
The adequacy of sample size is important to clinical trials. In the planning phase of a trial, however, the investigators are often quite uncertain about the sizes of parameters which are needed for sample size calculations. A solution to this problem is mid-course recalculation of the sample size during the ongoing trial. In internal pilot study designs, nuisance parameters are estimated on the basis of interim data and the sample size is adjusted accordingly. This review attempts to give an overview on the available methods.
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September 12, 2008
Chris Jennison (University of Bath)
Comparing adaptive designs and the classical, group sequential approach to clinical trial design
We shall consider the objectives of adaptive and group sequential designs and compare these approaches in two applications. The first case study concerns sample size re-estimation in response to interim estimates of the treatment effect. Our conclusion here is that group sequential methods provide an adequate mechanism to curtail or prolong a study as information accrues about the primary endpoint. In the second case study we focus on adaptation of the patient population in response to interim results and see tangible benefits from the adaptive approach: moreover, the issues here are beyond the remit of group sequential tests. The talk will conclude with an attempt to generalise from these two examples.
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October 10, 2008
Zachary Skrivanek (Eli Lilly)
A seamless 2/3 design incorporating a clinical utility index
Diabetes is a disease with well understood and validated biomarkers that have been used in clinical trials for decades to assess the safety and efficacy of diabetes therapies. Consequently, adaptive designs are well suited for learning about the dose response of a diabetes drug and providing confirmatory evidence for the safety and efficacy of the optimal dose(s). We will discuss the design of a novel adaptive, inferentially seamless Phase 2/3 study for an experimental drug to treat diabetes. The design employs a Bayesian Analytical approach to allocate patients to a set of doses of the experimental drug and to determine if there are 1-2 doses that could be continued to be studied for the purposes of "confirming" safety and efficacy of those doses. The preference for a dose is determined by a Clinical Utility Index, which balances the select efficacy and safety measures. The algorithm is completely pre-specified and the operating characteristics were assessed via simulation. This design was developed through iterative simulations that involved key decision makers who would normally have input as to what doses were to be selected for confirmatory trials. It involved much more apriori planning than would be required for a typical fixed design. We will discuss the differences between this approach of designing a study and the traditional fixed design approach. We will also discuss the mathematical form of the Clinical Utility Index and compare it to alternative derivations.
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November 14, 2008
Armin Koch (Medical University of Hannover)
Adaptation and heterogeneity: how much is too much?
This presentation is followed by a discussion by Paul Gallo (Novartis) and Willi Maurer (Novartis)
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December 12, 2008
Mohan Beltangady (Pfizer), Michael Brown (Pfizer)
Enhanced clinical trial design: How it has transformed our late stage drug development
Enhanced Clinical Trials Initiative which was started in 2005 has transformed the way we do Late Stage Development at Pfizer. Its major features are 1) Model-based Quantitative Drug Development, 2) Improved Statistical Designs including adaptive trials, and 3) Knowledge Management Systems. The principles adopted and the behavioral changes in the clinical team emphasizing the triad (troika) of clinician-pharmacologist-statistician have been critical to the success of cultural changes in operating model resulting in over $100M in savings for the Global Drug Development at Pfizer since 2006. In this presentation we share some of the lessons of implementing such impactful initiatives in a large pharma R&D organization.
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