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Modeling data with structural and temporal correlation using lower level and higher level multilevel models
Author(s) -
James Gareth,
Zhou Yinghui,
Miller Sam
Publication year - 2011
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.479
Subject(s) - multilevel model , computer science , correlation , hierarchical database model , variation (astronomy) , data mining , statistics , econometrics , machine learning , mathematics , physics , geometry , astrophysics
Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter‐relationships, but the most appropriate statistical analysis for handling this data is often uncertain – precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two‐level and four‐level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied. Copyright © 2010 John Wiley & Sons, Ltd.