9. Exploring Longitudinal Data

  1. Peter M. Fayers1,2 and
  2. David Machin3,4

Published Online: 8 MAR 2002

DOI: 10.1002/0470846283.ch9

Quality of Life: Assessment, Analysis and Interpretation

Quality of Life: Assessment, Analysis and Interpretation

How to Cite

Fayers, P. M. and Machin, D. (2000) Exploring Longitudinal Data, in Quality of Life: Assessment, Analysis and Interpretation, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/0470846283.ch9

Author Information

  1. 1

    Medical Research Council Clinical Trials Unit, London, UK

  2. 2

    Unit of Applied Clinical Research, Norwegian University of Science and Technology, Trondheim, Norway

  3. 3

    NMRC Clinical Trials & Epidemiology Research Unit, Singapore

  4. 4

    School of Health and Related Research, University of Sheffield, UK

Publication History

  1. Published Online: 8 MAR 2002
  2. Published Print: 18 APR 2000

ISBN Information

Print ISBN: 9780471968610

Online ISBN: 9780470846285



  • percentage;
  • mean;
  • median;
  • histogram;
  • box plot;
  • summary profile;
  • reverse profiles;
  • variabilities;
  • tabular presentations


The majority of studies involving QoL assessment include repeat assessments over time. Thus, in a randomised trial and other studies there may be a baseline assessment, followed by a series of further assessments during the active treatment period, followed by (often less frequent) further assessments. QoL data are therefore longitudinal in form, the analysis and presentation of which is relatively straightforward if the number of observations per patient is equal. However, for QoL assessment this will seldom if ever be the case. The final dataset will usually be very ragged with the numbers of assessments available for analysis differing from patient to patient. In this chapter we introduce a summary of such data by means of the area under QoL curve. However, the main focus is on how such longitudinal data can be represented in a graphical format.