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.
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.
Using adaptive design in clinical trials has many advantages. A quick summary of the advantages are as follows:
There are many types of adaptive trials designs. One popular type is that of sample size re-estimation (SSR).
Recommended Reading:
The Advantages & Disadvantages of
Blinded Sample Size Re-Estimation
This article provides insight on when to choose this design for your trial.
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What can be changed during an adaptive trial relies on the pre-planning design stage. The frequently included items are:
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 |
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.
Statistical quantity |
Fixed-design
|
Feature with
|
Potential
|
| 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 |
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.
See why nQuery is the leading sample size software solution for adaptive clinical trials.
Take a free online trial of nQuery now
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.
In this webinar you’ll learn about:
References:
Adaptive designs in clinical trials: why use them, and how to run and report them | BMC Medicine, 2018.
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