Documents
Resources
Learning Center
Upload
Plans & pricing Sign in
Sign Out

Background to Adaptive Design Nigel Stallard Professor of Medical

VIEWS: 17 PAGES: 15

									Background to Adaptive Design

             Nigel Stallard
          Professor of Medical Statistics
  Director of Health Sciences Research Institute
              Warwick Medical School
            n.stallard@warwick.ac.uk
                       Outline

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
        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 T1, ..., Tk
  Control treatment T0
      Stage 1                         Stage 2
        T0                             T0
        T1                             T[1]
                      Select
        T2         treatment(s)        
                                      Tk 2 
        Tk

  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
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 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

								
To top