Causal inference in multi-state models-sickness absence and work for 1145 participants after work rehabilitation.
Causal inference in multi-state models-sickness absence and work for 1145 participants after work rehabilitation.
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DOI：
10.1186/s12889-015-2408-8
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发表时间：
2015-10-23
影响因子：
4.5
通讯作者：
Aalen OO
中科院分区：
文献类型：
--
作者：
Gran JM;Lie SA;Øyeflaten I;Borgan Ø;Aalen OO
多状态模型作为传统生存分析模型的扩展，已被证明是一个灵活的框架，用于分析各种疾病缺勤和工作状态之间的过渡。在本文中，我们研究了一个队列的工作康复的参与者，并分析他们随后的病假，使用挪威的疾病福利登记数据。我们的目的是研究如何详细的个人协变量的问卷调查信息解释差异病假和工作，并使用因果推理的方法来评估干预措施的效果，以减少病假。后者的例子包括评价部分时间病假与全时病假的使用情况，以及估计合作协定对更具包容性的工作生活的影响。使用考克斯比例风险和Aalen加性风险模型估计协变量调整的转移强度，而使用逆概率加权和G计算方法评估干预措施的效果。协变量调整分析的结果显示，假设高风险和低风险协变量特征的患者在病假和工作方面存在很大差异，例如基于年龄、工作类型、收入、健康评分和诊断类型。因果分析表明，部分与全职病假的影响很小，有一个合作协议的积极影响，约5%的概率高返回工作岗位。详细的协变量信息对于解释不同疾病缺勤和工作状态之间的转换非常重要，对于患者特定队列也是如此。因果推断方法可以提供从协变量特定估计到多状态模型中的总体平均效应所需的工具，并根据干预措施直接解释确定因果参数。
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. 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 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. 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.