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Generalised estimating equations and low back pain
  1. E F Harkness1,
  2. E S Nahit1,
  3. G J Macfarlane1,
  4. A J Silman1,
  5. J McBeth1,
  6. G Dunn2
  1. 1Arthritis Research Campaign Epidemiology Unit, Stopford Building, Medical School, University of Manchester, Oxford Road, Manchester M13 9PT, UK; moeyjefh@fs1.ser.man.ac.uk
  2. 2Biostatistics Group, Medical School, University of Manchester

    https://doi.org/10.1136/oem.60.5.378-a

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      We read with interest the article by Hoogendoorn et al who examined the use of different approaches to analysing data from their prospective cohort study of work related exposures and the future onset of low back pain.1

      Exposures and outcomes are time dependent factors as they are subject to change over time. The strength of the relation depends on the assumptions of time dependence (or independence) of exposures and outcomes. The effects of these assumptions can be investigated by adopting different modelling approaches to studies that have collected repeated measures of exposure and outcome data over time.

      Hoogendoorn et al have adopted such an approach in their study of work related risk factors for low back pain.1 Information on work related physical and psychosocial factors and low back pain outcome was collected at baseline and in three annual follow ups. They showed an increased risk of low back pain for work related mechanical factors, when using two different generalised estimating equation (GEE) models compared to the standard logistic regression approach.1 Conversely, for work related psychosocial factors the association with low back pain was weaker when the GEE method was employed. Such an approach is enlightening and we agree that it is important to explore such analytical techniques in the investigation of work related risk factors and musculoskeletal symptoms. Therefore further exploitation of this method of analysis seems appropriate.

      We have recently conducted a …

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