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Instrumental variable specifications and assumptions for longitudinal analysis of mental health cost offsets

机译：用于纵向分析精神卫生费用抵消的工具性变量规范和假设

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摘要

Instrumental variables (IVs) enable causal estimates in observational studies to be obtained in the presence of unmeasured confounders. In practice, a diverse range of models and IV specifications can be brought to bear on a problem, particularly with longitudinal data where treatment effects can be estimated for various functions of current and past treatment. However, in practice the empirical consequences of different assumptions are seldom examined, despite the fact that IV analyses make strong assumptions that cannot be conclusively tested by the data. In this paper, we consider several longitudinal models and specifications of IVs. Methods are applied to data from a 7-year study of mental health costs of atypical and conventional antipsychotics whose purpose was to evaluate whether the newer and more expensive atypical antipsychotic medications lead to a reduction in overall mental health costs.
机译：借助工具变量（IV），可以在存在无法测量的混杂因素的情况下获得观测研究中的因果估计。在实践中，可能会带来各种各样的模型和IV规范，尤其是纵向数据，在纵向数据中可以估计当前和过去治疗的各种功能的治疗效果。然而，实际上，尽管IV分析做出了无法由数据最终检验的强大假设，但很少检查不同假设的经验结果。在本文中，我们考虑了IV的几种纵向模型和规范。方法应用于非典型和常规抗精神病药的7年心理健康成本研究中的数据，其目的是评估更新和更昂贵的非典型抗精神病药是否导致总体心理健康成本降低。

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