Comparison of two different approaches for the analysis of data from a prospective cohort study: an application to work related risk factors for low back pain

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Abstract
Aims: To compare the results of a traditional approach using standard regression for the analysis of data from a prospective cohort study with the results of generalised estimating equations (GEE) analysis. Methods: The research was part of a three year prospective cohort study on work related risk factors for low back pain. The study population consisted of a cohort of 1192 workers with no low back pain at baseline. Information on work related physical and psychosocial factors and the occurrence of low back pain was obtained by means of questionnaires at baseline and at the three annual follow up measurements. In a traditional standard logistic regression model, physical and psychosocial risk factors at baseline were related to the cumulative incidence of low back pain during the three year follow up period. In a GEE logistic model, repeated measurements of the physical and psychosocial risk factors were related to low back pain reported at one measurement point later. Results: The traditional standard regression model showed a significant effect of flexion and/or rotation of the upper part of the body (OR = 1.8; 95% CI: 1.2 to 3.0), but not of moving heavy loads (OR = 1.4; 95% CI: 0.7 to 3.1). The GEE model showed a significant effect of both flexion and/or rotation of the upper part of the body (OR = 2.2; 95% CI: 1.5 to 3.3) and moving heavy loads (OR = 1.5; 95% CI: 1.0 to 2.4). No significant associations with low back pain were found for the psychosocial work characteristics with either method, but the GEE model showed weaker odds ratios for these variables than the traditional standard regression model. Conclusions: Results show that there are differences between the two analytical approaches in both the magnitude and the precision of the observed odds ratios.