Adaptive Budgets in Clinical Trials.

Area of Science:

• Clinical trial design
• Pharmaceutical development
• Biostatistics

Background:

• Clinical trials often encounter interim analyses that may trigger additional funding.
• This funding can be strategically allocated to optimize trial outcomes and drug registration probabilities.

Purpose of the Study:

• To investigate optimal sample size allocation strategies for clinical trials receiving additional funding after a promising interim analysis.
• To evaluate the impact of different funding allocation approaches on overall trial power and drug development success.

Main Methods:

• Analysis of two sample size adjustment strategies: full allocation to the second stage and a utility function-based approach.
• The utility function incorporates the reward of drug registration, dependent on observed effect size, and sampling costs.

Main Results:

• Increasing sample size when conditional power is already high may offer diminishing returns on overall power.
• A utility function approach can reveal multiple local maxima for sample size optimization, including at the lowest acceptable regulatory size.
• For small effect sizes, employing the minimum regulatory sample size may be an optimal strategy.

Conclusions:

• The optimal allocation of additional financial resources in clinical trials depends on the observed interim effect size and trial stage.
• A utility function-based approach provides a more nuanced strategy for sample size determination than simply increasing sample size.
• Drug developers should consider regulatory requirements and cost-benefit analyses when deciding on sample size adjustments to maximize the probability of successful drug registration.