Newsletter | September 9, 2021

09.09.21 -- Strategies For Optimizing Clinical Trial Designs

Mathematical Methods For Clinical Trial Financial Strategy

When Cyrus Mehta introduced the Promising Zone Design over a decade ago, the new statistical method not only transformed the allocation of scarce resources within a clinical trial setting but also reconceptualized how sponsors could increase investment in their trials. Since then, the propulsion of simulation technology and the power of new forecasting engines has generated even more methods to align clinical development with financial strategy.

Identifying Choice Trial Designs Using Pareto

Economists have long used the concept of Pareto Efficiency to refer to the optimal allocation of resources with respect to a set of preference criteria. Learn how the idea of Pareto Efficiency, also called Pareto Optimality, can be used for the strategic selection of a clinical trial design, rapidly identifying which designs are most aligned with an organization’s business goals, such as speed-to-market, savings in development costs, and likelihood of success.

A Data-Infused Approach To De-Risking Clinical Trials

For many decades, the Pareto Frontier has been employed by actors in the private sector to evaluate and understand the benefits of various strategic options. This article describes two functional uses of the Pareto concept for clinical trials — the selection of a de-risked trial design, and an improved understanding of the financial nature of the tradeoffs between various operational parameters of clinical trial design.

Selecting A Clinical Trial Design: How Broadly Should You Explore?

When selecting clinical trial designs, how many design options should a sponsor explore? Would a sponsor feel more confident having an optimized design out of ten potential ones, or simulating several million designs to discover that there are three or four better options? Here are five considerations for sponsors when making this complex decision.

Use Of Scoring Functions For Clinical Trial Optimization

Scoring functions enable trial sponsors to rank clinical trial designs by performance characteristics ranging from statistical power to sample size, trial duration, and even estimated clinical trial costs. This article provides an overview of how scoring functions can be implemented for enhanced clinical trial selection.

Aligning Commercial Strategy And Statistical Design
Da Volterra wanted to maximize the power of their clinical trial for scenarios with a smaller treatment effect and were therefore contemplating a sample size re-estimation design. Unfortunately, with a strict enrollment limit of 1,100 patients, this particular study design produced only marginal gains in power over the fixed sample size design. Learn how Da Volterra utilized a collaborative decision-support software platform to maximize the chance of a successful trial result.