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dc.contributor.authorGran, Jon Michaelen_US
dc.contributor.authorLie, Stein Atleen_US
dc.contributor.authorØyeflaten, Irene Larsenen_US
dc.contributor.authorBorgan, Ørnulfen_US
dc.contributor.authorAalen, Odd O.en_US
dc.PublishedBMC Public Health 2015, 15:1082eng
dc.description.abstractBackground Multi-state models, as an extension of traditional models in survival analysis, have proved to be a flexible framework for analysing the transitions between various states of sickness absence and work over time. In this paper we study a cohort of work rehabilitation participants and analyse their subsequent sickness absence using Norwegian registry data on sickness benefits. Our aim is to study how detailed individual covariate information from questionnaires explain differences in sickness absence and work, and to use methods from causal inference to assess the effect of interventions to reduce sickness absence. Examples of the latter are to evaluate the use of partial versus full time sick leave and to estimate the effect of a cooperation agreement on a more inclusive working life. Methods Covariate adjusted transition intensities are estimated using Cox proportional hazards and Aalen additive hazards models, while the effect of interventions are assessed using methods of inverse probability weighting and G-computation. Results Results from covariate adjusted analyses show great differences in sickness absence and work for patients with assumed high risk and low risk covariate characteristics, for example based on age, type of work, income, health score and type of diagnosis. Causal analyses show small effects of partial versus full time sick leave and a positive effect of having a cooperation agreement, with about 5 percent points higher probability of returning to work. Conclusions Detailed covariate information is important for explaining transitions between different states of sickness absence and work, also for patient specific cohorts. Methods for causal inference can provide the needed tools for going from covariate specific estimates to population average effects in multi-state models, and identify causal parameters with a straightforward interpretation based on interventions.en_US
dc.publisherBioMed Centraleng
dc.rightsAttribution CC BYeng
dc.subjectMulti-state modelseng
dc.subjectCausal inferenceeng
dc.subjectCohort studyeng
dc.titleCausal inference in multi-state models–sickness absence and work for 1145 participants after work rehabilitationen_US
dc.typePeer reviewed
dc.typeJournal article
dc.rights.holderCopyright Gran et al. 2015
dc.subject.nsiVDP::Medisinske Fag: 700en_US

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