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Myths surrounding the use of synthetic control arms in clinical trials: Part 3

Synthetic control arms (SCAs) leverage real world data from various sources or evaluations of historical clinical data to demonstrate the positive effects of a new therapy or treatment, without the need to use a placebo or standard of care as a control. Advanced statistical methods are applied to historical trial or real world data to build the SCA in a way that allows for the appropriate comparison with data gathered during the execution of the single arm trial. However, is the use of advanced methodologies inevitable for the success of SCAs? Majority of statistical and mathematical theories used for the design of SCAs are decades old and familiar to the scientific community.

In Part 3 of this blog series on SCA mythbusters, we talk about the use of advanced analytics and the impressions that many groups have in relation to some of the more innovative statistical methodologies which are used in the SCA space.

Myth 3: Advanced statistics are needed for successful SCAs

There are certainly some applications, data challenges and issues which need to be solved using more complex statistical methodologies. However, it is not always the case that more is better. Under certain conditions, studies can be statistically straightforward. There are instances of regulatory submissions which have utilized less evolved statistics. For example, a product called Brineura was approved for Batten disease on the basis of a Phase I/II single arm trial, compared to a natural history study. Clear regulatory narrative, solid statistical analysis and large effect size helped in obtaining regulatory approval.

There are certain statistical methodologies which can be useful if you run into specific data challenges. But there are also some fundamental data issues which cannot be accounted for. Some of these challenges are intrinsic to the study and the core of it has to be dealt with at the study planning stage.

From a regulatory perspective, if you can show through straightforward, interpretable analyses that you have a reasonable effect size and evidence of benefit, and you can produce the same results using a couple of different methodologies, then that makes the submission stronger. The greater the reach to adjust and use surrogates, the more challenging regulatory discussions can become. Advanced methodologies have their place, but they cannot supersede quality study planning and their use should depend on the project you are undertaking.

Click on the button to watch Louis Dron, Senior Director at Cytel, explain this matter in further details.

Access on DemandRead Part 1. Read Part 2.


About 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.


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