A circular LEAR correlation structure for cyclical longitudinal data.

Pubmed ID: 21216801

Journal: Statistical methods in medical research

Publication Date: June 1, 2013

Affiliation: Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA. slsimpso@wfubmc.edu

MeSH Terms: Humans, Models, Theoretical, Blood Pressure Monitoring, Ambulatory, Hypertension, Randomized Controlled Trials as Topic

Authors: Simpson SL, Edwards LJ

Cite As: Simpson SL, Edwards LJ. A circular LEAR correlation structure for cyclical longitudinal data. Stat Methods Med Res 2013 Jun;22(3):296-306. Epub 2011 Jan 7.

Studies:

Abstract

Circular covariance patterns arise naturally from many important biological and physical processes. Modelling these patterns can be immensely important for proper analyses. In this article, we propose a circular linear exponent autoregressive (LEAR) correlation structure for cyclical longitudinal data. Special cases of this parsimonious correlation model include the equal correlation and first-order moving average (MA(1)) correlation structures and a circular analogue of the continuous-time AR(1) model. We discuss properties and estimation of the circular LEAR model in the context of cyclical longitudinal data concerning diet and hypertension (the DASH study). Analysis of these data exemplifies the benefits of the circular LEAR correlation structure.