多人群人口统计学和全球健康结果的时间模型：引入新框架，以审查和标准化模型假设的文档并促进模型比较
Temporal models for demographic and global health outcomes in multiple populations: Introducing a new framework to review and standardize documentation of model assumptions and facilitate model comparison
摘要：
人们越来越有兴趣在数据有限的人群中编制人口和全球健康指标的估计数。需要统计模型将来自多个数据源的数据合并为具有不确定性的估计和预测。不同的建模方法已被应用到这个问题，使模型之间的比较困难。我们提出了一个模型类，时间模型多人口（TMMP），以方便文档的模型假设在一个标准化的方式和比较模型。该类别区分潜在趋势和观察到的数据，这些数据可能是嘈杂的或表现出系统性偏差。我们提供的过程模型，它描述了潜在的趋势感兴趣的指标的一般配方。我们展示了如何现有的各种指标的模型可以写为TMMP和如何TMMP为基础的描述可以用来比较和对比模型的假设。最后，我们讨论了悬而未决的问题和未来的方向。
英文摘要：
There is growing interest in producing estimates of demographic and global health indicators in populations with limited data. Statistical models are needed to combine data from multiple data sources into estimates and projections with uncertainty. Diverse modeling approaches have been applied to this problem, making comparisons between models difficult. We propose a model class, Temporal Models for Multiple Populations (TMMPs), to facilitate documentation of model assumptions in a standardized way and comparison across models. The class distinguishes between latent trends and the observed data, which may be noisy or exhibit systematic biases. We provide general formulations of the process model, which describes the latent trend of the indicator of interest. We show how existing models for a variety of indicators can be written as TMMPs and how the TMMP-based description can be used to compare and contrast model assumptions. We end with a discussion of outstanding questions and future directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
