Triggered sampling could help improve longitudinal studies of persons with elevated mortality risk

One approach to overcoming potential biased results, due to dropout from longitudinal clinical studies, is to capture additional data once a marker of health downturn is observed but before the patient leaves the study. We denote this study design feature as "triggered sampling" (TS).

We formally define TS, describe some mechanisms for incorporating TS in longitudinal studies, and present the results from a 2-year longitudinal observational study of treatment preferences, measured on a 1-7 scale, of patients with advanced illness from cancer, congestive heart failure, or chronic obstructive pulmonary disease. We examined the utility of TS through multiple analyses, including mixed effects models.

One hundred forty-eight of 226 participants experienced at least one triggered interview. Those who did not drop out after their first trigger had no noticeable change in their mean preferences (6.20 pretrigger, 6.16 trigger, P=0.76), whereas those who dropped out after their first trigger did (6.29 pretrigger, 5.69 trigger, P=0.04). The mixed effects models conveyed similar results, providing support for the efficiency and efficacy of TS.

TS can help alleviate bias due to impending dropout and potentially be a valuable addition to the designs of longitudinal studies of persons with elevated mortality risk.