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Simulation enhanced distributed lag models for mortality displacement

机译:用于死亡率位移的仿真增强型分布式滞后模型

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摘要

Distributed lag models (DLM) are attractive methods for dealing with mortality displacement, however their estimates can have substantial bias when the data is generated by a multi-state model. In particular DLMs are not valid for mortality displacement. Alternative methods are scarce and lack feasibility and validation. We investigate the breakdown of DLM in three state models by means of simulation and propose simulation enhanced distributed lag models (SEDLM) to overcome the defects. The new method provides simultaneous estimates of the net effect (entry) and the displacement effect (exit). These have improved performance over the singular estimate from a regular DLM. SEDLM entry estimates have negligible bias and their variance is reduced. The exit estimates are unbiased and their variance is one order of magnitude lower with respect to the entry estimates. Applying SEDLM to the original Chicago data, the 95% highest posterior density intervals for both entry and exit contain 0, providing neither evidence for a ‘displacement effect’ nor for a ‘net effect’. Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-016-3566-6) contains supplementary material, which is available to authorized users.
机译:分布式滞后模型(DLM)是处理死亡率位移的有吸引力的方法,但是,当数据由多状态模型生成时,其估计值可能会产生重大偏差。特别是DLM不适用于死亡率替代。替代方法很少,缺乏可行性和验证性。我们通过仿真研究了三种状态模型中DLM的故障,并提出了仿真增强型分布式滞后模型(SEDLM)以克服缺陷。新方法同时提供了净效应(入口)和位移效应(出口)的估计。与常规DLM的单个估计相比,它们的性能有所提高。 SEDLM条目估计值的偏差可以忽略不计,并且方差减小了。出口估算是无偏的,并且其方差相对于入口估算低一个数量级。将SEDLM应用于原始的芝加哥数据,进入和退出的最高后验密度间隔95%均包含0,既没有提供“位移效应”也没有“净效应”的证据。电子补充材料本文的在线版本(doi:10.1186 / s40064-016-3566-6)包含补充材料,授权用户可以使用。

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