Transcript What are adaptive designs?

Background to Adaptive Design
Nigel Stallard
Professor of Medical Statistics
Director of Health Sciences Research Institute
Warwick Medical School
[email protected]
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
T[1]
T1
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