Reducing neonatal and child mortality is a global priority. In countries without comprehensive vital registration data to inform policy and planning, statistical modelling is used to estimate the distribution of key causes of death. This modelling presents challenges given that the input data are few, noisy, often not nationally representative of the country from which they are derived, and often do not report separately on all of the key causes. As more nationally representative data come to be available, it becomes possible to produce country estimates that go beyond fixed-effects models with national-level covariates by incorporating country-specific random effects. However, the existing frequentist multinomial model is limited by convergence problems when adding random effects, and had not incorporated a covariate selection procedure simultaneously over all causes. We report here on the translation of a fixed effects, frequentist model into a Bayesian framework to address these problems, incorporating a misclassification matrix with the potential to correct for mis-reported as well as unreported causes. We apply the new method and compare the model parameters and predicted distributions of eight key causes of death with those based on the previous, frequentist model.

中文翻译：

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具有综合协变量选择和误分类矩阵的贝叶斯分层模型，用于估计新生儿和儿童的死因

降低新生儿和儿童死亡率是全球优先事项。在没有全面的生命登记数据来为政策和规划提供信息的国家，统计模型被用来估计主要死因的分布。考虑到输入数据很少、嘈杂、通常不能代表数据来源国家的全国情况，并且通常不会单独报告所有关键原因，因此该模型存在挑战。随着越来越多的具有全国代表性的数据可用，通过纳入国家特定的随机效应，可以产生超越具有国家级协变量的固定效应模型的国家估计。然而，现有的频率多项式模型在添加随机效应时受到收敛性问题的限制，并且没有针对所有原因同时纳入协变量选择程序。我们在此报告将固定效应、常客模型转化为贝叶斯框架以解决这些问题，并结合错误分类矩阵，并有可能纠正错误报告和未报告的原因。我们应用新方法并将八个主要死因的模型参数和预测分布与基于以前的频率论模型的模型参数和预测分布进行比较。