5 Ways to Optimize Trial Designs

1 - Better Design

Every design choice will have some effect on the required sample size
Good designs are efficient and thus reduce sample size
Design decisions include - randomization level (Subject level Vs Cluster level etc)
- Crossover vs parallel (Crossover design can be more efficient if chronic disease)
- Choice of endpoint - closer endpoint is to actual measure the better
- Some unneeded simplifications increase costs - dictomisation, treating TTE as binary, recurrent events as TTE
- Covariate Selection should be part of the study design and protocol

Using RWE can help researchers determine a viable study length and frequency of study events when designing their trial
Enable researchers to assess site feasibility from previous data from site-specific clinical trials that were successful
Create Synthetic Control Arms and therefore allow all recruited patients to be in the active arm
Trial planners can assess and model protocols to reduce potential patient issues which lead to dropout, prior to commencing the trial

Frequentists can gain greater insight and ideally make better decisions to complement their frequentist trial with Bayesian assurance
Take advantage of prior information and expert opinion
Bayesian (Power) Assurance - “True Probability of Success” very useful at financial & scientific Review boards.
Can help you formalize your sample size sensitivity analysis

Adaptive Designs can lead to more efficient clinical trials
Group Sequential Design & Sample Size Re-estimation facilitates interim analyses where you can either stop for benefit or futility or increase your sample size if the effect size is promising
Other Adaptive areas include enrichment, seamless trials and dose finding
Adaptive Trials can reduce costs and risks and result in earlier decisions
Patients may also favour enrolling in adaptive trials because of the increased probability they will receive more effective treatment

The basket trial design tests a single oncology drug in various populations
The umbrella trial design is used to evaluate multiple investigational oncology drugs in a single disease population
Ability to “bundle” treatments and garner economies of scale (reduce paperwork and duplication of activities)
From the perspective of the sites there can be a great reduction in overhead resulting in significant cost savings
Evaluate safety and efficacy of treatments in parallel instead of sequentially
Reduce patient exposure to control arms