The Clinical Trial Risk Tool has been featured in a guest column in Clinical Leader, titled A Tool To Tackle The Risk Of Uninformative Trials, in cooperation with Abby Proch, Executive Editor at Clinical Leader.
In the article, Thomas Wood of Fast Data Science highlights the problem of “uninformative” clinical trials – those that don’t provide meaningful results, even if the drug being tested is effective or ineffective. He distinguishes these from simply “failed” trials and emphasises the ethical and financial waste they represent. Wood explains that while “uninformativeness” lacks a formal definition, it can be understood by examining the five conditions of an “informative” trial as outlined by Zarin, Goodman, and Kimmelman (2019): addressing an important question, meaningful design, feasibility, scientific validity, and timely, accurate reporting. Trials excluded from meta-analyses due to bias are often considered uninformative.
Wood describes how the Clinical Trial Risk Tool tackles this problem by assessing trial protocols against these criteria. He suggests expanding the tool to include a template clinical trial budget derived from real-world cost data (e.g., Sunshine Act disclosures). Further enhancements could include identifying endpoints and inclusion/exclusion criteria, then searching clinical trial registries (like ClinicalTrials.gov) for similar past trials to help users evaluate their planned trial’s design choices.
Wood also suggests tailoring the tool for different user profiles (patient advocates, financial planners, medical professionals) by providing personalised feedback and recommended actions for protocol improvement. The goal is not to replace human review, but to help users identify design gaps and high-risk indicators early in the process.
Fast Data Science is a leading data science consultancy firm providing bespoke machine learning solutions for businesses of all sizes across the globe, with a concentration on the pharmaceutical and healthcare industries.
Over the years, the overall cost of the drug development process has been exponentially increasing, prompting the adoption and use of adaptive clinical trial design software. Though there are practical difficulties and barriers in implementing clinical trial solutions, these problems are adequately addressed to overcome these issues as they arise. With advancements in software technologies, further improvements are being made to the software’s adaptive clinical trial design. Despite these progresses, just only a handful of well-established software with various types of clinical trial adaptations is currently available.
A clinical trial protocol is a document which serves as the step-by-step playbook for running the trial. The clinical trial protocol guides the study researchers to run the clinical trial effectively within a stipulated period. The prime focus of the clinical trial protocol is to ensure patients’ safety and data security. [1, 2] As the clinical trial protocol is an essential document for the seamless execution of the clinical trial, reviewing (peer-reviewing) the protocol is essential to ensure the scientific validity/viability/quality of the protocol.
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Introduction As we move toward 2025, clinical trial regulations are undergoing significant transformation. This shift is being fueled by technological advancements, changing healthcare needs, and an increasing emphasis on transparency and patient safety. In this post, we will explore the key clinical trial regulations shaping the clinical trial landscape, the challenges professionals face, and the strategies they must adopt to navigate this ever-evolving environment.
