Longitudinal study design to efficiently estimate biomarker change-point outcomes and time-to-change-point

It is common to make longitudinal observations, repeated measurements over time, of biomarkers (and other covariates). Rather than smoothly changing over time, some biomarkers may exhibit sharp changes in their behaviour; a change-point is an identifiable shift in the long-run biomarker level. In some settings the abrupt change in the biomarker, rather than the specific level, is the primary outcome of interest. For example, monitoring cognition in older individuals, there is a distinct rapid decline in cognitive ability linked with dementia and other cognitive impairments, beyond the normal age-related decline. Rather than assessing a treatment’s effect on preventing a change in cognition, another outcome would be to delay the change to so-called rapid decline. A treatment might fail to prevent rapid decline, but could delay the time until onset of rapid decline; to test this involves analysing the time to the change-point (between normal age-related decline and rapid decline of cognition).

This is a non-linear longitudinal model of the biomarker and is appropriate when the trajectory of the biomarker and its change-point are of interest as a clinical outcome, rather than simply the overall change or simply the time until change. Estimation of the change-point in cognitive decline is well established in the cognition literature, using the Mini-Mental State Exam (MMSE), but there is little research on designs with the time to a change-point as the outcome of interest. The aim of the project is to investigate novel study designs that adapt the number and interval of observations for each individual and to demonstrate whether this leads to an increased power to detect change-points within a biomarker.