Adaptive study designs allow for prospectively planned modifications to the design based on interim data analysis in order to increase efficiency. This is more flexible than conventional designs but also more complex. Key types of adaptations include sample size re-estimation, dropping treatment arms, and adapting doses or endpoints. Advantages include obtaining the same information more efficiently and improving understanding of treatment effects. However, concerns relate to increased type I error rates and challenges in interpretation. Regulatory perspectives are still evolving around adaptive designs. Careful planning and control mechanisms are needed to balance flexibility with scientific integrity.
3. Defining adaptive study
design
Guidance for Industry: Adaptive Design Clinical
Trials for Drugs and Biologics
‘a study that includes a prospectively planned
opportunity for modification of one or more
specified aspects of the study design and
hypotheses based on analysis of data (usually
interim data) from subjects in the study ’
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without undermining the validity and integrity of
the trial
7. Types of adaptations
Trial procedures
Eligibility criteria
Study dose – therapy regimen
Duration of treatment
Study endpoints
Lab testing procedures
Diagnostic procedures
Concomitant treatments used
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8. Types of adaptations
Trial procedures
Planned schedule of the patient evaluations
evaluations for the data collection (eg.
number of the intermediate time points,
timing of the last patient observation and
duration of the patient study participation)
Criteria for evaluation and assessment of
clinical responses
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9. Types of adaptations
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Statistical procedure
Randomization
Study design
Study hypothesis
Sample size
Data monitoring
Interim analysis
Statistical analysis plan/
10. Well understood Adaptive
Study Designs
• Adaptation of Study Eligibility Criteria Based
• Adaptations to Maintain Study Power
• Adaptations based on the interim results of an
outcome unrelated to efficacy
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11. Well understood Adaptive
Study Designs
• Adaptations Using Group Sequential Methods
and unblinded Analyses
• Adaptations based on the Data Analysis Plan
• Adaptation for the primary endpoint
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24. Advantages over
Conventional Study Designs
• Same information more efficiently
• More success
• Improved understanding of treatment effect
• CSD – inaccurate estimates or assumptions
• Decrease cost and time by discontinuing a group
• Adjust sample to avoid an undesired powered
study
• Assessment of more choices within same time
frame
• No protocol amendment
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25. Comparing Adaptive and
Conventional study designs
Features Conventional
trial
Adaptive design
Design More rigid Flexible
Treatment arms Max. 2 – 3 Many
simultaneously
Hypothesis Test the
hypothesis
under
consideration
Fit data into
hypothesis
Modifications Not allowed
without protocol
amendments
Pre-specified
allowed
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26. Comparing Adaptive and
Conventional study designs
Features Conventional
trial
Adaptive design
Phases Phases I – II are
well defined
Can be seamless
phase 2/3
Statistical
analysis
Frequentist
approach
Complicated
Bayesian
approach
Organization Much simple Complicated
Interim analysis Not routine Routinely and
frequently
Regulatory view Well endorsed speculative 26
27. Concerns associated with
Adaptive study designs
• Increased type I error
• Difficult interpretation of results
• More planning and lead time
• Develop into a completely different trial
• Incorporation of a totally different population
• Bayesian statistics
• Computer based simulations
• Challenge to CROs and EC
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28. Concerns associated with
Adaptive study designs
• Lack of definition by regulatory authorities
• Bayesian approach is compulsion – non
standard
• Damage to integrity
• Magnify bias with increase in sample size
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29. Procedural issues
• Rapid electronic collection of data
• Efficient interaction b/w investigator and
sponsor
• Financial investments
• Computer based simulations with
infrastructure and software
• CRO & EC lack experience of monitoring
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30. Regulatory perspective
• Acceptable level of adaptation?
• Regulatory standards for review and approval
process of clinical data?
• A completely new clinical trial post
modification?
• Increased interaction b/w FDA and sponsors
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34. Conclusion
• Many advantages
• Infant stage – many areas are controversial
• Not an ad hoc measure for poor planning
• Strong promise for the future
• More guidelines and better understanding
• Complexities limit use
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The figure illustrates a hypothetical example of a study in which sample size re-estimation due to uncertainty about the standard deviation σ led to an increase in sample size to ensure 90% power was maintained. At the beginning of the trial, the planned sample size was estimated at 150 patients based on a standard deviation of 1.0. At the interim analysis, the actual standard deviation was 1.4. Even though the effect size (d) was as originally predicted, an increase in sample size to 295 patients would be required to maintain 90% power. Without the sample size re-estimation, the power at the final analysis would only be 64% and there would be much greater risk of a failed trial. LPFV, last patient first visit.