Biological age study data capture: epigenetic clock eCRF template

Longevity and healthspan studies often use a biological age estimate as an exploratory or secondary outcome, alongside clinical measures such as blood pressure, laboratory panels and questionnaires. The estimate itself arrives as a number, or a set of numbers, on a report from a laboratory or vendor. The data problem is rarely the number. It is everything around it: which sample produced it, whether it was collected under the same conditions each time, which vendor and which method version generated it, and how it links to the participant's other data.

In a small study that context often lives in an email thread and a spreadsheet. By the time a statistician needs it, a sample ID has been retyped, a vendor changed its method between baseline and follow-up, and nobody can say which. A form attached to the visit, with required fields and an audit trail, prevents most of it. Our EDC for longevity clinical trials page covers the wider workflow for these studies.

A participant sampled at baseline and again at, say, month 6 and month 12 produces several rows of the same structure. Because each visit carries its own form with the same fields, the dataset is already in the long format an analyst wants, and an exported data dictionary explains each variable.

Longitudinal comparison depends on consistency. If the baseline sample is processed by one laboratory and the follow-up by another, or the vendor updates its method in between, a difference in the numbers may reflect the process, not the participant. Recording the vendor, assay name and method version on every form makes that visible so the analysis plan can handle it, and the audit trail records any later correction with a reason.

Sample collection conditions matter in the same way. Time of day, fasting status, collection method and storage delay can all differ between sites, so capture them as fields rather than free-text notes. For multi-site studies, a site-level participant numbering scheme and by-site exports make site differences easy to review; see wearable data integration for linking this form with continuous device data in the same study.

Time between samples is another quantity worth structuring. Define the visit schedule so each sample falls within a window around the planned day, and use the visit windows in the schedule to flag late or early collection. Participants who start a new intervention, supplement or medication between samples should have that recorded on the concomitant medications eCRF, so the analyst can see what changed. A short vital signs form at each sampling visit gives context for the biological age value without adding a second system.

Finally, treat the vendor's report as source. Attach or store the original report according to your source data plan, enter the values from it, and use source data verification on the fields that feed your endpoints. The result of any clock stays the vendor's result; your study documents what was reported and when.