Strategies for Implementing Flexible Clinical Trials Jerald S. Schindler, Dr.P.H. Cytel Pharmaceutical Research Services 2006 FDA/Industry Statistics Workshop 28 September 2006

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Interest in Flexible Trials Recently there has been much interest in Flexible (Adaptive) Trials Opportunity for –Better quality decisions –More data focused on the ‘interesting’ treatments –Reduced risk of clinical development –Fewer patients required for development –Reduced cost

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Adaptive Trials - Definition A clinical trial process that: uses data not available at study start as a basis for modifications to the trial design.

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Adaptive Strategy Maximize information collected on effective doses. Minimize information collected on non-effective doses

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 “Adaptive” is a Process Change Adaptive trials are part of an Adaptive Drug Development Process –Incorporates - early review of clinical data –Option for modifications to a trial in progress –All data from the trial are used in the analysis Does not refer to a particular statistical methodology

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Key Adaptive Modifications Sample size adjustment Change the randomization fraction –To favor certain treatments –To begin avoid others Add new treatment arms Eliminate treatment arms

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Statistical Techniques Bayesian methods Group sequential methods –Classical –New ‘Adaptive’ methods –Combining p-values

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Example – Sample Size Adjustment Test of a new drug –    –  is unknown may be between –    then N = 1051 may be too large –    then N = 263 may be too small If the true value of  = 3 then Power = 68% Adapted from Mehta and Patel, Statistics in Medicine, 2006 Fixed Sample size

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Example – Sample Size Adjustment Sample size adjustment using Group sequential methods –Plan to enroll up to 1063 patients –Stop early for efficacy –Plan to monitor sequentially up to 3 times – at n = 354, 709, 1063 Now if  prob of stopping at 709 is 93% Now if  prob of stopping at 354 is 93% Adapted from Mehta and Patel, Statistics in Medicine, 2006

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Potential Errors - Concerns Early decisions based on preliminary data may lead to incorrect conclusions (Decisions based on the entire trial may also be incorrect) The goal in an adaptive development process is to –Accept that some decisions will be wrong –Over time the value of an early decision supports the process (i.e. More correct decisions than incorrect ones) –Use appropriate decision rules that permit reasonable trial modifications

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Adaptive Strategy Constrain Flexibility over drug development time Options for great flexibility in “Early Development” Reduced flexibility in “Registration Development”

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Constrained Options Early DevelopmentRegistration Development Sample size Add treatment Drop treatment Randomization fraction Modify delta Endpoint Patient population Sample size Randomization fraction Drop treatment Modify delta Submission Adaptive Phase IIAdaptive Phase III

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Constrained Options Early Development Registration Development Sample size Add treatment Drop treatment Randomization fraction Modify delta Endpoint Patient population Sample size Randomization fraction Drop treatment Modify delta Submission Adaptive Phase IIAdaptive Phase III Constrain: Type of options Range of choices

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Early Development Explore many doses Explore multiple sub-populations Patient or disease characteristics Create a multi-dimensional matrix of options to explore Range of variables to explore Range of levels within each variable

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Early Development Too many cells to explore completely Assess each cell as: –Not interesting –Possible –Clearly interesting Abandon the “Not interesting” cells early Likely use Bayesian methods to select cells Promote a select subset for Phase III

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Tandem Adaptive Clinical Trials Early Development Registration Development Sample size Add treatment Drop treatment Randomization fraction Modify delta Endpoint Patient population Sample size Randomization fraction Drop treatment Modify delta Submission 1.Adaptive Phase II Trial POC/Dose Response Estimation 2. Adaptive Phase III Trial

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Tandem Adaptive Clinical Trials Early Development Sample size Add treatment Drop treatment Randomization fraction Modify delta Endpoint Patient population Submission 1.Adaptive Phase II Trial POC/Dose Response Estimation

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Tandem Adaptive Clinical Trials Early Development Submission 1.Adaptive Phase II Trial POC/Dose Response Estimation Doses

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Tandem Adaptive Clinical Trials Early Development Submission 1.Adaptive Phase II Trial Patient Populations – Gene expression Patient Groups

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Tandem Adaptive Clinical Trials Early Development 1.Adaptive Phase II POC/Dose Response Estimation Doses 1.Bayesian allocation for treatment assignments 2.Frequentist analysis at end

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Tandem Adaptive Clinical Trials Early Development Registration Development Submission 1.Adaptive Phase II Trial POC/Dose Response Estimation 2. Adaptive Phase III Trial Doses

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Tandem Adaptive Clinical Trials Registration Development Submission 2. Adaptive Phase III Trial Is this a Phase II/III trial or simply a Phase III trial?

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Tandem Adaptive Clinical Trials Registration Development Submission 2. Adaptive Phase III Trial Phase II/III implies limited data from early trials. Here we have a lot of information from early trials. I would call this an Adaptive Phase III trial.

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Tandem Adaptive Clinical Trials Early Development Registration Development 1.Adaptive Phase II POC/Dose Response Estimation 2. Adaptive Phase III Doses

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Data Access - Implementation Capability EDC eClinical Online review IVRS, Drug supply, Randomization Integration      

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Interim Analyses When do you look? How many times? What are the decision rules?

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Simulation Assumptions: –Response in placebo and each treatment group –Accrual rate –Time for endpoint collection Evaluate: –Different interim analysis time points –Different decision rules Repeat Choose from the results for the actual trial

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Interim Analysis Who can see the data? For Phase II –Few issues For Phase III –Risk of bias is generally low –Added value of an unblinded sponsor statistician is low –Decisions tend to be driven by algorithms Current procedures adequate –Best if no sponsor involvement in IDMC –If absolutely necessary - Minimize opportunity for ‘data leaks’

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Lessons Learned Not all adaptive methods are new, unfamiliar, or complicated Try the familiar methods first –Group sequential, Bayesian, ‘adaptive’ –Need to scale – how can we handle hundreds of trials? Current IDMC procedures can be used –Minimize unblinded data access for Phase III

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Lessons Learned Build a process with a workflow –Plan –Simulate –Design –Monitor –Adapt Simulations for planning –When, how often, decision rules Build capability for rapid data access and review

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Lessons Learned Adaptive Phase II –Broadly focused –Many different types of adaptations –Many different levels for each adaptation –Bayesian methods for allocation –Less need to protect the blind Design the overall program – not single trials in isolation

Jerald S. Schindler, Dr.P.H FDA/Industry Statistics Workshop 28 September 2006 Lessons Learned Adaptive Phase II –Broadly focused –Many different types of adaptations –Many different levels for each adaptation –Bayesian methods for allocation –Less need to protect the blind Adaptive Phase III –Narrow focus –Fewer types of adaptations –Fewer levels for each adaptation –More control of data access –Retire the phrase “Seamless Phase II/III” Design the overall program – not single trials in isolation