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  • Poster presentation
  • Open Access

Introducing framework for analyzing non-adherence (FAN)

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Trials201516 (Suppl 2) :P32

  • Published:


  • Public Health
  • Clinical Trial
  • Treatment Medication
  • Research Effort
  • Structural Design

The importance of patient adherence to treatment medication cannot be over emphasised. Some authors have claimed adherence is the secret to a successful clinical trial; intuitively, non-adherence can lead to a trial failure. Non-adherence in clinical trials does not only blur drug efficacy results but could also have huge financial implications; research efforts are incessantly being made to find a “cure” to it.

Fault Tree Analysis (FTA) is a simple way of determining how the combination of basic faults of a system can lead to a total system failure. Initially, it was used in electrical, mechanical, computer and other engineering fields to determine how combinations of components of a system can cause a total system failure. Due to its usefulness, it has been employed in non-engineering fields.

In this work, we introduce the Framework for Analyzing Non-adherence (FAN) and demonstrate how it can improve adherence. FAN is an offline analysis platform which harnesses the strengths of FTA in identifying potential causes of non-adherence and providing valuable information on how to improve the structural design of a clinical trial with adherence in mind.

A software implementation of FAN, called the FAN Tool, is under development. Based on non-adherence information supplied by users, the FAN Tool will allow them to identify rated areas of non-adherence. This will give investigators useful information on where to invest their resources to boost adherence, thereby improving their overall trial success.

Authors’ Affiliations

University of Glasgow, Glasgow, UK


© Edifor et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.


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