We recently spoke with Dr. Eric J. Daza about his work as a biostatistician and data scientist at the forefront of digital health innovation. Daza is a member of DATAcc by DiMe’s Validating Novel Digital Clinical Measures Statistical Advisory Committee (SAC), a distinguished group of experts who developed a standardized framework for validating digital health measures. This project bridges clinical, regulatory, and industry needs, accelerating the adoption of reliable, novel research and patient care measures. 

Beyond his work with DATAcc, Daza is passionate about advancing digital health through personalized analytics. He is also the creator of the Stats-of-1 newsletter and podcast that explores n-of-1 statistical methods and their transformative applications in digital health, personalized medicine, and person-centered data-driven insights. Here’s what he had to share with us:

Why was it important for you to join this committee?

Ambiguity around digital health measures adds noise to evaluating the safety and efficacy of interventions and therapies that use data from digital health technologies (DHTs) like sensors and apps. Lack of agreement on featurization techniques and analysis tools blocks true scientific consensus and objectivity.

This applies to verifying the relevance and quality of a DHT and its data and determining how usable, analytically valid, and clinically meaningful they are. DATAcc created the V3+ framework. It is a rigorous foundation that standardizes these verification and validation steps, one of which is the analytical validation of DHT-derived measures.

Suppose we test an innovative solution that uses a DHT-derived measure. The results look great—but did we measure the correct quantities? To truly improve care, the answer must be “yes.” This committee and our work are critical because they will help craft the validation standards to answer this question.

The digital health community will undoubtedly benefit. I am especially excited about how the resulting scientific and regulatory alignment will accelerate modern personalized n-of-1 studies and other emerging quantitative idiographic or “esametric” approaches that use DHTs. These designs address the inherently heterogeneous effects of interventions from the “bottom-up”: unlike randomized controlled trials, studies of real-world evidence (RWE), and much of today’s AI, esametric methods prioritize your health history over others’ in determining the most suitable treatment plan or regimen for you in particular.

Which resource do you find especially valuable?

The study builder for Novel Digital Clinical Measure Analytical Validation Planning stands out as a key deliverable because it guides the creation of a project plan—a document essential for conducting good science. A well-articulated plan ensures replicable and reproducible results.

From my experience leading and contributing to numerous digital health projects, particularly in the industry, I know that the most successful ones empowered all team members to plan effectively. Clear instructions on writing, reviewing, and updating key documents, like the initial proposal and project plan, were integral to the project’s success.

How are you planning to use the resources?

Many digital health projects already conduct exploratory analyses to characterize how novel digital clinical measures might relate to established reference measures. The natural next step is to conduct a formal analytical validation study.

The study builder makes it easy to propose this follow-up study to clinical and business partners. It gives me a full project template outlining the benefits of such a study and the logistics. The simulation code is a ready-to-go resource for illustrating various scenarios to help clients appreciate why we need an analytical validation study.

The study builder is a structured worksheet that divides the entire validation project into sequential stages. It enables the project team to create and align proposals and plans effectively. The team can clearly define objectives, tasks, roles, responsibilities, and deliverables by working through its prompts and flowcharts. This iterative approach improves communication among project partners, streamlines operations, and ensures deliverables are completed and delivered on time to satisfied clients and sponsors.

Where do you see these resources delivering the most impact on the field?

These analytical validation resources benefit industry groups  – including those in pharma, biotech, medical device, and digital health companies – coordinating with the FDA and other regulatory bodies to define, operationalize, collect, and analyze digitally recorded measures. 

Groups focused on patient-centered outcomes and digital health will directly benefit, as will some RWE teams. The structured workflow provides a standardized and rigorous foundation for creating widely accepted digital endpoints for clinical studies and trials. Specifically, the framework helps these groups and teams better align with sponsors and regulators on what needs to be done and how to accomplish it.

Get involved with DATAcc

DATAcc projects focus on advancing digital health measurements by developing new evidentiary frameworks and measures in a pre-competitive environment. Join a broader group of leaders across the healthcare ecosystem to establish and execute a shared vision of high-quality digital health measurement that is easily accessible and beneficial for everyone. Learn about joining an upcoming project here.