LAUSR

Meaningful inference in epidemiology relies on accurate exposure measurement. In longitudinal observational studies, having more exposure data in the form of repeated measurements in the same individuals adds useful information. But exactly how much do repeated measurements add, incremental to the information provided by baseline measurements? In this issue of the Journal, Paige et al. (Am J Epidemiol. 2017;186(8):899-907 have quantified the value of adding repeated cholesterol and blood pressure measurements to baseline measurements in a meta-analysis of individual participant data from 38 longitudinal cohort studies. Repeated measurements improve prediction significantly, but the magnitude of this gain in information may be less than expected. In research studies and clinical practice, quality of measurement is more important than quantity.