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Leveraging External Data for Efficient Trial Designs

The main challenge associated with the development of therapies for rare diseases is typically the small study sample sizes. Other difficulties when studying rare diseases include uncertainties around disease history, target patient profiles, and the existence of appropriate comparators. Further complications may arise when developing novel therapies and disease targets where the choices of the most appropriate primary endpoint(s) is unknown or not well established. So, how can we properly test a new drug in patients with a rare tumor type?

In a recent Cytel webinar presented by Satrajit Roychoudhury, PhD, Senior Director, Statistical Research and Data Science Centre at Pfizer, a new approach is proposed that allows the use of external information for time-to-event data, even when patient level data is not available.

Let us take the example of a scientist who wants to compare a new treatment in a rare cancer. They will want to find out how to do a reasonably well controlled trial in this rare population. The trial has to be designed to efficiently address the scientific question of interest, with the available resources and timeline. It will be upon the statisticians to investigate further aspects (for example, alternative endpoint, nature of the disease, nature of treatment effect) to provide suitable design recommendations.

One way to design such a trial is to reduce the control by borrowing external information and perform a comparison between treatment arm and control arm. The second option is to conduct a single arm trial using standard of care or comparator drug to do the comparison.

Using external control data is becoming increasingly popular. However, it still requires assessment of similarity between external and concurrent control. We need to address questions such as, how to borrow and how much we can borrow. Although, these designs are popular, they are not commonly used, which leads to several concerns such as, acceptance by regulators, relevance of data, heterogeneity for data source, complexity of time to event data and a reasonable false positive rate.

In the webinar, a novel methodology is introduced to resolve the above statistical concerns. Dr. Roychoudhury proposes a Bayesian meta-analytic approach to leverage historical data for time-to-event endpoints, which are common in oncology and cardiovascular diseases. The approach is based on a robust hierarchical model for piecewise exponential data. It allows for various degrees of between trial-heterogeneity and for leveraging individual as well as aggregate data. Dr. Roychoudhury uses ovarian carcinoma trial data and a phase II non-small cell cancer trial design to illustrate the methodology along with essential practical aspects.

Recent healthcare and regulatory changes are supportive of such innovative designs.  For example, The 21st Century Cures Act propagates innovations to accelerate the discovery, development, and delivery of 21st century cures using modern, more innovative methods. It also includes the broader application of Bayesian statistics and the use of evidence from clinical expertise.

To learn more about the new approach, watch the on demand webinar and register for the other Bayesian webinars in this series.

Watch On Demand

The increase in single arm trials in oncology and rare diseases has created challenges when it comes to comparing treatments. In response, the use of synthetic and external control arms has grown dramatically, enabled by improved access to data including statistical toolkits from many research fields.

At Cytel, we select the most appropriate data source based on the requirements of the treatment comparison to be performed. We then harness the latest advances in Bayesian dynamic borrowing, propensity score adjustment and epidemiological microsimulation modelling to derive synthetic controls capable of standing up to clinical and regulatory scrutiny.

Download our new ebook that explains common strategies for the construction of synthetic control arms and gives a high-level overview of how statisticians think through challenges encountered during trial design.


PicsArt_09-18-02.23.33About the Author of Blog:

Mansha Sachdev specializes in content creation and knowledge management. She holds an MBA degree and has 11 years of experience in handling various facets of marketing, across industries. At Cytel, Mansha is a Content Marketing Manager and is responsible for producing informative content that is related to the pharmaceutical and medical devices industries.