11. Missing Data

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

Published Online: 8 MAR 2002

DOI: 10.1002/0470846283.ch11

Quality of Life: Assessment, Analysis and Interpretation

Quality of Life: Assessment, Analysis and Interpretation

How to Cite

Fayers, P. M. and Machin, D. (2000) Missing Data, in Quality of Life: Assessment, Analysis and Interpretation, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/0470846283.ch11

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

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Keywords:

  • missing data;
  • bias;
  • mean imputation;
  • hierarchical scales;
  • regression imputation;
  • Markov chain imputation;
  • hot deck imputation;
  • cold deck imputation;
  • EM algorithm

Summary

This chapter describes problems that arise through missing QoL assessment data. Situations are outlined where values are missing from otherwise completed questionnaires or where entire forms are missing. The main difficulty with either type of missing data is the bias they may introduce at the analysis stage. QoL data that are “missing” through attrition because the patient has died, from that which could be anticipated but was not returned, are distinguished. How missing values may be estimated, often termed as imputed, to ease the statistical analysis are described, but stress that imputing values is no substitute for collecting “real” data.