Christensen et al. demonstrated that frailty models had higher statistical power than standard methods. Combining parametric models with frailty models may be a powerful tool in sickness absence research. Alternatively, multi-state models may be a useful application to sickness absence research. In multi-state models it is possible to model individuals moving among a finite number of stages, for example from work to sickness absence to work disability
or back to work again. Stages can be transient or absorbing #HSP activation randurls[1|1|,|CHEM1|]# (or definite), with death being an example of an absorbing state. To each of the possible transitions covariates can be linked. In multi-state models assumptions can be made about the dependence of hazard rates on time (Putter et al. 2007; Meira-Machado et al. 2008; Lie et al. 2008). Our results are relevant for see more further absence research in which the application of parametric hazard rate models should be encouraged. It is
important to visualize the baseline hazard and detect risk factors which are associated with certain stages in the sickness absence process. Using these models, groups at risk of long-term absence can be detected and interventions can be timed in order to reduce long-term sickness absence. The choice of a parametric model should be theory-driven instead of data-driven. The current study gives a promising impulse to the development of such a theory. Acknowledgments The authors wish to thank Prof. Dr. ir. F.J.C. Willekens (Professor of Demography at the Population Research Center, University of Groningen)
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