Invited Commentary: Quantifying the Added Value of Repeated Measurements

Andrew E. Moran*, Kiang Liu

*Corresponding author for this work

Research output: Contribution to journalReview article

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)908-909
Number of pages2
JournalAmerican journal of epidemiology
Volume186
Issue number8
DOIs
StatePublished - Oct 15 2017

Keywords

  • Cardiovascular disease
  • Longitudinal measurements
  • Repeated measurements
  • Risk factors
  • Risk prediction

ASJC Scopus subject areas

  • Epidemiology

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