A random-censoring Poisson model for underreported data
A random-censoring Poisson model for underreported data
复制标题
DOI：
10.1002/sim.7456
复制
发表时间：
2017-12-30
影响因子：
2
通讯作者：
Assuncao, Renato Martins
中科院分区：
文献类型：
--
作者：
de Oliveira, Guilherme Lopes;Loschi, Rosangela Helena;Assuncao, Renato Martins
监测欠发达国家社会贫困地区的风险时面临的一个主要挑战是，经济、流行病学和社会数据通常被低估。因此，不考虑数据质量的统计模型将产生有偏见的估计。为了解决这个问题，可疑地区的计数通常被视为经过审查的信息。删失的泊松模型是可以考虑的，但所有的删失区域必须是精确的先验知识，这在大多数实际情况下是不合理的假设。我们引入了随机删失泊松模型(RCPM)，该模型解释了计数和数据报告过程的不确定性。因此，对于每个区域，我们将能够估计感兴趣事件的相对风险以及审查概率。为了便于后验抽样过程，我们提出了一种基于数据增强技术的马尔可夫链蒙特卡罗方法。我们对所提出的RCPM模型和两个竞争性模型进行了仿真研究。考虑了不同的场景。应用RCPM和删失泊松模型来解释巴西米纳斯吉拉斯州地区早期新生儿死亡率的潜在漏报，这些地区的数据质量已知较差。
A major challenge when monitoring risks in socially deprived areas of under developed countries is that economic, epidemiological, and social data are typically underreported. Thus, statistical models that do not take the data quality into account will produce biased estimates. To deal with this problem, counts in suspected regions are usually approached as censored information. The censored Poisson model can be considered, but all censored regions must be precisely known a priori, which is not a reasonable assumption in most practical situations. We introduce the random-censoring Poisson model (RCPM) which accounts for the uncertainty about both the count and the data reporting processes. Consequently, for each region, we will be able to estimate the relative risk for the event of interest as well as the censoring probability. To facilitate the posterior sampling process, we propose a Markov chain Monte Carlo scheme based on the data augmentation technique. We run a simulation study comparing the proposed RCPM with 2 competitive models. Different scenarios are considered. RCPM and censored Poisson model are applied to account for potential underreporting of early neonatal mortality counts in regions of Minas Gerais State, Brazil, where data quality is known to be poor.