Background to Adaptive Design Nigel Stallard Professor of Medical Statistics Director of Health Sciences Research Institute Warwick Medical School

1.What are adaptive designs? Types of adaptive designs 2.Advantages and challenges Advantages Statistical challenges Logistical challenges 3.Example – adaptive seamless design in MS Adaptive seamless phase II/III clinical trial Evaluation of design options 4.Implications for research funders Outline

1. What are adaptive designs? Conventional fixed sample size design Start Observe data Clinical trial reality: gradual accumulation of data Start Observe data Adaptive design: Use interim analyses to assess accumulating data Adapt design for remainder of trial

Types of adaptive designs Possible adaptations can include: - “Up-and-down” type dose-finding - Adaptive randomisation (rand. play-the-winner etc.) - Sample size re-estimation based on nuisance parameter estimates - Sample size re-estimation based on efficacy estimates (including ‘self-designing trials’) - Early stopping for futility - Early stopping for positive results - Selection or modification of subgroups or treatments - Stopping for safety or logistical reasons

Focus on methods for confirmatory trials: - Sample size re-estimation based on nuisance parameter estimates - Sample size re-estimation based on efficacy estimates (including ‘self-designing trials’) - Early stopping for futility - Early stopping for positive results - Selection or modification of subgroups or treatments

2. Advantages and challenges Advantages Efficiency: - reach conclusion with (on average) smaller sample size - avoid wasting further resources on trials unlikely to yield useful results - ensure trials are appropriately powered - focus resources on evaluation of most promising treatments Ethics: - use right number of right patients on right treatments

Statistical challenges Type I error rate E.g. Interim analysis in phase III trial to compare two arms Significant at 5% level – stop trial Not significant – continue with trial Probability of false positive at interim analysis = 5% Overall probability of false positive > 5% Other adaptations may also increase type I error rate e.g. sample size increased after less promising interim data

Treatment effect estimation Trial may stop because of extreme positive data Conventional estimates will overestimate true treatment effect Specialist statistical methodology is required

Logistical challenges Up-front planning Designs may be more ‘custom-made’ Design properties may need to be assessed prior to trial e.g. by simulation studies Management of unblinded data Breaking of blind may lead to bias, limit recruitment or lead to lack of equipoise Release of information and decision-making process needs to be carefully considered

Conduct of interim analyses Timely and accurate data management required Trial modification May require ethical approval May require revision of patient information sheets Randomisation and drug supply needs careful consideration

3. Example – Adaptive seamless design in MS Setting Primary/secondary progressive Multiple Sclerosis Challenges No current effective disease modifying therapy Several potential novel drug therapies to evaluate Outcomes ‘Phase II’ Short-term MRI data (~6-12 months) ‘Phase III’ Long-term disability scales (~2-3 years) Clinical trials are very long and costly

Adaptive seamless phase II/III clinical trial Experimental treatments T 1,..., T k Control treatment T 0 Select treatment(s) at interim analysis using MRI data Final analysis uses combination test to control overall type I error rate allowing for selection/multiple testing Stage 1 T 0 T 1 T 2  T k Stage 2 T 0 T [1]  Select treatment(s)

Evaluation of design options Choice of design options sample size, timing of interim analysis, decision rule for selecting arms Simulation study estimate power to reject at least one false null hypothesis estimate selection probabilities based on wide range of assumptions treatment effect on primary outcome treatment effect on short-term outcome correlation between outcomes from extensive literature review 10,000 simulations for each of > 25,000 scenarios

Example simulation results 3 experimental treatments Interim analysis midway early one effective treatment one partly effective

4. Implications for research funders Advantages Adaptive designs could lead to efficiency gains Resources are targeted most effectively Challenges Need to ensure appropriate methodology is used Additional methodological development may be needed May need to allow extra time/funding for design work and evaluation More flexible trials may require more flexible funding model