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Open Access

Comparison of analysis approaches for multi-level vascular imaging data

  • Julia Forman1,
  • Marie Fisk2,
  • Joseph Cheriyan2,
  • Simon Bond1, 3 and
  • Ian Wilkinson1, 2
Trials201516(Suppl 2):P237

Published: 16 November 2015

Analysis methods for clinical trials with imaging endpoints are not well established. Here we will present and compare methods to analyse vascular inflammation as measured by 18F-FDG PET/CT imaging.

These methods have been developed for the EVOLUTION trial: An Evaluation of Losmapimod in patients with Chronic Obstructive Pulmonary Disease (COPD) with systemic inflammation stratified using fibrinogen (EudraCT Number 2011-004936-75), a randomised, double-blind, placebo-controlled trial. Patients were randomised to 7.5 mg losmapimod or matching placebo tablets twice daily for four months.

Vascular inflammation was assessed by 18F-FDG PET/CT scan at baseline and following treatment. Each scan covers up to six vessel segments per participant (left carotid, right carotid, and four aortic segments) and each segment is analysed in “slices”, generating a three-level data structure: participants, vessels, slices. For each slice, the mean and maximum inflammation level is recorded (as measured by the tissue-to-background ratio of the 18F-FDG tracer).

Here, we consider three analysis approaches to assess the treatment effect. 1: Data are pooled by vessel and by patient, by considering the change in the mean (or max) inflammation. 2: The most inflamed vessel segment in each patient is identified at baseline and followed-up in each patient. 3: A multi-level model incorporates the complete data set and data structure. Through comparing these analysis approaches, we aim to identify a method which provides an accurate measure of the treatment effect, and a straightforward interpretation.

Authors’ Affiliations

Cambridge Clinical Trials Unit, Cambridge University Hospitals, Cambridge, UK
Division of Experimental Medicine and Immunotherapeutics, Cambridge University Hospitals, Cambridge, UK
MRC Biostatistics Unit, Cambridge, UK


© Forman et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.