Estimating the effect of latent time-varying count exposures using multiple lists.
Estimating the effect of latent time-varying count exposures using multiple lists.
复制标题
使用多个列表估计潜在的时变计数暴露的影响。
DOI：
10.1093/biomtc/ujad027
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发表时间：
2024
期刊：
影响因子：
1.9
通讯作者：
Sánchez,BrisaN
中科院分区：
文献类型：
--
作者：
Won,JungYeon;Elliott,MichaelR;Sanchez-Vaznaugh,EmmaV;Sánchez,BrisaN
纵向建筑环境健康研究的一个主要挑战是用于描述动态食品环境特征的商业数据库的准确性。由于来源可信度的不同，不同的数据库往往对同一主题提供相互冲突的曝光度量。由于历史数据无法进行现场验证，我们建议结合多个数据库来纠正任何一个数据源的测量误差造成的健康效应估计偏差。我们提出了一个随时间变化的健康结果、观察到的计数暴露和潜在的真计数暴露的联合模型。我们的模型估计了源的时间特异性质量，并通过泊松整值一阶自回归过程结合了真计数暴露的时间依赖性。我们采用贝叶斯非参数方法来灵活地考虑特定地点的暴露。通过解决不同数据库之间的不一致性，我们的方法减少了真实暴露对纵向健康影响的偏差。我们的方法用2001年至2008年加州公立学校的儿童肥胖数据与学校附近便利店的暴露情况进行了验证。
A major challenge in longitudinal built-environment health studies is the accuracy of commercial business databases that are used to characterize dynamic food environments. Different databases often provide conflicting exposure measures on the same subject due to different source credibilities. As on-site verification is not feasible for historical data, we suggest combining multiple databases to correct the bias in health effect estimates due to measurement error in any 1 datasource. We propose a joint model for the time-varying health outcomes, observed count exposures, and latent true count exposures. Our model estimates the time-specific quality of sources and incorporates time dependence of true count exposure by Poisson integer-valued first-order autoregressive process. We take a Bayesian nonparametric approach to flexibly account for location-specific exposures. By resolving the discordance between different databases, our method reduces the bias in the longitudinal health effect of the true exposures. Our method is demonstrated with childhood obesity data in California public schools with respect to convenience store exposures in school neighborhoods from 2001 to 2008.