Trial@home
Methods & challenges

Trial@home

Trial@home, CHDR’s dedicated strategy for off-site clinical trials, enables investigators to collect information as trial participants go about their day to day activities.

Establishing that medicines work, in the sense that they influence a certain pathophysiological mechanism, is a lot easier than demonstrating that they help. To show that they help, it is crucial to include value-based evaluation. This requires follow up of patients who do not necessarily have events of a condition (e.g. a stroke). Trial@home enables this by using technology and assuring that data is reliably collected while trial participants are not in the clinic.

What CHDR offers

Trial design and execution services

With more than 30 years of experience in designing and executing clinical trials, CHDR assures that your trial is designed to the highest scientific standards.

Data analytics services

Non‑clinic data require a different data analytics approach. With in‑house expertise in Artificial Intelligence and biological signal processing, CHDR assures that all information in the collected data is extracted for a better evaluation of the trial outcomes.

Validated and novel digital endpoints

With a scientific background, CHDR assures that all novel digital endpoints that are developed are properly validated with a rigorous scientific approach.

Seamless integration with digital technology

Digital technology has been emerging in the healthcare and drug development world. CHDR is constantly looking for candidate technologies to be integrated in our custom‑built platform. Thorough validation of the technology is performed (in terms of relevance of the measure, data quality and transparency, ease of use for trial participants) before integration.

Integration with our Electronic Data Capturing system (Anju Promasys)

To ease transfer of data, we have full integration with our EDC system that allows endpoints to be ready for statistical analysis and transfer as soon as possible.

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Publications

Trial@home in our library

PHD Thesis

Development of machine learning – derived mhealth composite biomarkers for trial@home clinical trials

Ahnjili ZhuParris

Key publication

A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity.

Browse all related publications 32

Advancing the boundaries of clinical drug development

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