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What Are Adaptive Clinical Trials? (and why use them)

January 28, 2019

What are adaptive designs in clinical trials and why use them? Let us quickly look at the advantages of adaptive designs in clinical trials and what they mean for you.

What are adaptive clinical trials?

What are Adapative Clinical Trials

Adaptive trials are any trial where a change or decision is made to a trial while the trial is still on-going. It is said that trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants.

["Adaptive designs in clinical trials: why use them, and how to run and report them" is a recent open access article available from BMC Medicine that is referenced in this blog post and linked below]

Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. Historically the uptake of adaptive clinical trials has been somewhat slow. However, the rising costs of clinical trials in conjunction with the high failure rate has led the pharmaceutical industry to seek out new innovative methods. The FDA has recently published updated guidance on this topic and is now actively encouraging sponsors to engage with them in regards an adaptive design.

This video is an excerpt from our webinar The Advantages & Disadvantages of Adaptive Sample Size Re-Estimation.

What are the advantages of adaptive design clinical trials?

Using adaptive design in clinical trials has many advantages. A quick summary of the advantages are as follows:

Slide9

What changes can occur in an adaptive or flexible designed clinical trial?

What can be changed during an adaptive trial relies on the pre-planning design stage. The frequently included items are:

  • Refining the sample size
  • Abandoning treatments or doses
  • Changing the allocation ratio of patients to trial arms
  • Identifying patients most likely to benefit and focusing recruitment efforts on them
  • Stopping the whole trial at an early stage for success or lack of efficacy.

What are different types of adaptive trial designs?

Adaptive Trial Design
Goal
Sample size re-estimation Adjust sample size to ensure the desired power
Group-sequential design Design to stop the trial early for safety, futility or efficacy
Multi-arm multi-stage Test multiple treatments, doses etc to select 'early winners' or 'drop losers'
Population enrichment Recruit patients more likely to benefit (most) from the treatment
Biomarker-adaptive Use biomarkers to inform or adapt the trial
Adaptive randomisation Shift allocation ratio towards more promising or informative treatment(s) 
Seamless phase I/II Combine safety and activity assessment into one trial
Seamless phase II/III Combine selection and confirmatory stages into one trial


What statistical issues do I need to consider with adaptive design?

The defining characteristic of all adaptive clinical trial designs is that the results from interim data analyses are used to modify the ongoing trial, without undermining its integrity or validity. As such, many statistical issues require great consideration when designing a trial that is firstly accepted by stakeholders and then continues to go on and be successful.

For a fixed randomised controlled trial (RCT) that is analysed using traditional statistics, it is common to present the estimated treatment effect (e.g. difference in proportions or means between treatment groups) alongside a 95% Confidence interval and p value. These are by no means the only relevant criteria for assessing the performance of a trial design but they are important statistical quantities for reporting a clinical trial. Below is a summary of how these may be affected by an adaptive design.
 
Statistical quantity
Fixed-design
RCT property
Feature with
adaptive design
Potential
solution
Effect
Estimate
Unbiased: on average (across many trials) the effect estimate will have the same mean as the true value Estimated treatment effect using naive methods can be biased, with an incorrect mean value Use adjusted estimators that eliminate or reduce bias; use simulation to explore the extent of bias
Confidence interval Correct coverage: 95% CIs will on average contain the true effect 95% of the time CIs computed in the traditional way can have incorrect coverage Use improved CIs that have correct or closer to correct coverage levels; use simulation to explore the actual coverage
p value Well-calibrated: the nominal significance level used is equal to the type I error rate actually achieved p values calculated in the traditional way may not be well-calibrated, i.e. could be conservative or anti-conservative Use p values that have correct theoretical calibration; use simulation to explore the actual type I error rate of a design

 

What do I need to consider for my sample size calculation?

As mentioned and expected, the methodology for sample size calculation for adaptive design differs from traditional trials. This is why researchers use dedicated sample size software for the task. nQuery has dedicated sample size calculation functionality for adaptive trials that contain a selection of sample size tables designed specifically for areas of adaptive design - such as sample size re-estimation.

Learn More About Adaptive Clinical Trials

We recently hosted a webinar examining Advantages & Disadvantages of Adaptive Sample Size Re-Estimation. You can watch this webinar on demand by clicking the image below.

Watch on demand now - The advantages of disadvantages adaptive sample size re-estimation in clinical trials

In this webinar you’ll learn about:

  • Advantages & Disadvantages of Adaptive Design
  • Evaluating Unblinded SSR for your trials
  • Evaluating Blinded SSR for your trials

References:
Adaptive designs in clinical trials: why use them, and how to run and report them | BMC Medicine, 2018.

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