Precision outcome measures are the means by which to evaluate a meaningful change on the exact outcome of interest, regardless of tool or instrument.
Today, researchers have tools that can capture an enormous variety of measurements and metrics. Advances in digital health, from imaging to remote monitors and more, have supplemented researchers’ toolboxes for understanding human health. With such an extensive suite of options, the question becomes: what should we measure?
Precision outcome measures unlock the full potential of today’s research technologies
A precision measures approach allows clinical researchers to activate the full range of tools at their disposal by starting with what outcomes are most meaningful to patients, and then zeroing in on the right tool for the job.
Take Sam’s experience, for example:
Meet Sam. They are participating in a clinical trial, and they have agreed to allow researchers at the sponsoring organization to collect data on their health using various mechanisms. The researchers are testing the efficacy of a new drug developed to treat a rare, recently discovered type of arrhythmia. As a part of the control group, Sam is participating to help the researchers develop a robust baseline to compare against those who are taking the drug.
Over the course of a typical week, Sam participates in the study both passively and actively. Their smartwatch automatically sends step counts, heart rate data, and sleep metrics directly to the research team’s database. Once a week, Sam stops by a local clinic to have a small amount of blood drawn and sent to research labs. And at the end of the day, Sam completes a questionnaire on their smartphone or laptop that asks about their day: how they felt, what kinds of exercises they did, and what foods they ate.
Researchers, like those in this fictional example, need to carefully choose the measures that matter to patients; they need to take into consideration the aspects of daily life that matter to the patient population they are trying to serve, the data points necessary to show evidence of drug efficacy, and the measures crucial to monitoring for adverse events. Designing study protocols with precise measures in mind gives the study a better chance of producing specific results, allowing for the safest drugs to get to market faster.
Thanks to the growing ecosystem of digital health technologies, there are now more ways than ever to collect precise measures from a diverse population. These technologies, often validated or developed using machine learning techniques, allow researchers to collect objective, digital biomarkers that can power new insights into the human condition.
While decentralized clinical trials may rely heavily on digital health technologies to remotely monitor patients, many trials and studies can include them as part of a broader suite of measurement tools deployed throughout the study. Labs, clinical outcome assessments like Sam’s patient-reported outcomes, imaging, genetics, and even real world evidence derived from a secondary source can all work together to build a more complete picture of our health.
From preventing disease to fostering wellness: precision outcome measures open new research possibilities
When we think about the purpose of medication, what comes to mind? For most, the answer is that medication is used to cure, slow, or prevent disease. But, what if we could move the research process upstream — extending our focus to not only preventing illness but also enabling us to feel alive and empowered to participate in the activities that bring us joy?
The rise of precision measures opens new opportunities for the life sciences. With the right measures, researchers can invert the research process; beyond preventing and curing disease, researchers can now look at ways to foster wellness and boost good health.
Pharmaceutical companies, biotechnology companies, and clinical research organizations are recognizing the importance of a measures-first approach. By starting with the “what” versus the “how”, the science can be driven by what is most meaningful to patient quality of life, and then matched with the fit-for-purpose tool for the job.
Imagine engaging Sam in the study design process early and often. With myrialinkedin.com/in/mr27d opportunities for measurement, how would a trial’s study design change if taking into account a study participant’s daily routines, feedback, and desired outcomes?
These questions, and the ability to answer them, are an exciting shift in the study design process, and one that ICON is proud to pioneer.
Blog authored by Matthew Ryan. Originally written for HumanFirst, a company acquired by ICON in January 2024.
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Digital Disruption
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Clinical trial data anonymisation and data sharing
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Clinical Trial Tokenisation
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Closing the evidence gap: The value of digital health technologies in supporting drug reimbursement decisions
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Digital disruption in biopharma
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Disruptive Innovation
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Personalising Digital Health
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The triad of trust: Navigating real-world healthcare data integration
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Patient Centricity
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Agile Clinical Monitoring
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Capturing the voice of the patient in clinical trials
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Charting the Managed Access Program Landscape
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The evolution of the Pharmacovigilance System Master File: Benefits, challenges, and opportunities
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A guide to safety data migrations
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An innovative approach to rare disease clinical development
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Decentralised and Hybrid clinical trials
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Biopharma perspective: the promise of decentralised models and diversity in clinical trials
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