Longitudinal study on work related and individual risk factors affecting radiating neck pain

E Viikari-Juntura; R Martikainen; R Luukkonen; P Mutanen; E-P Takala; H Riihimäki

doi:10.1136/oem.58.5.345

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Longitudinal study on work related and individual risk factors affecting radiating neck pain

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Comments on modelling longitudinal ordinal data
Anna E Ekman
Published on: 27 April 2016

Published on: 27 April 2016
Comments on modelling longitudinal ordinal data
- Anna E Ekman, Statistician
Dear Editor,
This article is presenting both the applied question and the statistical methods used in a very well organised and excellent way. Though, I have some comments on the use of the words risk, effect and predictor as these imply a casual relationship and make us believe that we model the development of neck pain.
The data in the article is longitudinal and in the analysis the authors are using this...
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Dear Editor,
This article is presenting both the applied question and the statistical methods used in a very well organised and excellent way. Though, I have some comments on the use of the words risk, effect and predictor as these imply a casual relationship and make us believe that we model the development of neck pain.
The data in the article is longitudinal and in the analysis the authors are using this. In the marginal model all repeated measures of “current neck pain” are included and time is in the model. In the transition model “current neck pain” is modelled and “neck pain previous year” is included in the model. These are interesting and valuable models that gives more information on the association of interest than a cross- sectional study would, but it is not the same as modelling risk or incidence.
The two models used and the analysis of them is correct as long as one sees that they are not models of the risk of developing neck pain. This fact is not clearly put forward either in this article or in texts about analysis of longitudinal ordinal data. The fact that the data is longitudinal ensures us that there is more information than data from a cross-sectional study can give. Note though that the fact that the data is longitudinal does not automatically imply that it is possible to analyse cause and effect or to analyse the risk of developing neck pain.
The marginal model in the article models the prevalence of neck pain using all the information in the repeated measures, time trend etc. The transition model is modelling the prevalence of neck pain and all the explanatory variables are adjusted for “previous neck pain”. That is the OR, for example of “mental stress”, is the OR for having neck pain now, averaged over all possible values of “previous neck pain”. Note though that this is not the same as modelling the development of neck pain, in other words the risk of getting neck pain. In the transition model one interaction is included, “previous neck pain” and “hand above shoulder”. This means that for the specific explanatory variable “hands above shoulder” one can talk about the effect on moving from severe to mild neck pain (developing neck pain) or the other way around due to “hands above shoulder”.
The topic of analysing longitudinal ordinal data as an alternative to dichotomising variables and analysing them traditionally is very interesting and needs further discussions. There are statistical models and methods available, but to realise what we model is of great importance.
I want to thank the authors for a nice article and for introducing these models in the area of occupational medicine. I look forward to further discussions and developments in the methodological area of longitudinal ordinal data.
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Conflict of Interest:
None declared.
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