Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict.
Model building and assessment of the impact of covariates for disease prevalence mapping in low-resource settings: to explain and to predict.
复制标题
DOI：
10.1098/rsif.2021.0104
复制
发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Diggle PJ
中科院分区：
文献类型：
--
作者：
Giorgi E;Fronterrè C;Macharia PM;Alegana VA;Snow RW;Diggle PJ
本文为基于模型的地统计方法的疾病流行绘图的开发和应用提供了统计指导。我们通过坦桑尼亚疟疾绘图的案例研究来说明分析的不同阶段，从探索性分析到流行率的空间预测。在整篇论文中，我们区分了预测模型和解释模型，预测模型的主要重点是最大化模型的预测准确性，而解释模型则更强调理解健康结果和风险因素之间的关系。我们证明这两种范式可以导致不同的建模选择。我们还提出了一种基于回归系数估计量的相关矩阵的检查来检测过度拟合的简单方法。为了增强地质统计模型的可解释性，我们引入了域效应的概念，以协助变量选择和模型验证。这里在疾病患病率绘图的特定背景下阐述的统计思想和原则更广泛地适用于任何用于分析流行病学结果的回归模型，但与地统计模型特别相关，对于地统计模型，固定效应和随机效应之间的分离可能不明确。
This paper provides statistical guidance on the development and application of model-based geostatistical methods for disease prevalence mapping. We illustrate the different stages of the analysis, from exploratory analysis to spatial prediction of prevalence, through a case study on malaria mapping in Tanzania. Throughout the paper, we distinguish between predictive modelling, whose main focus is on maximizing the predictive accuracy of the model, and explanatory modelling, where greater emphasis is placed on understanding the relationships between the health outcome and risk factors. We demonstrate that these two paradigms can result in different modelling choices. We also propose a simple approach for detecting over-fitting based on inspection of the correlation matrix of the estimators of the regression coefficients. To enhance the interpretability of geostatistical models, we introduce the concept of domain effects in order to assist variable selection and model validation. The statistical ideas and principles illustrated here in the specific context of disease prevalence mapping are more widely applicable to any regression model for the analysis of epidemiological outcomes but are particularly relevant to geostatistical models, for which the separation between fixed and random effects can be ambiguous.
登录
查看更多内容
影响因子：
3
作者：
Adigun AB;Gajere EN;Oresanya O;Vounatsou P
通讯作者：
Vounatsou P
影响因子：
3.2
作者：
Christiansen-Jucht, Celine D.;Parham, Paul E.;Basanez, Maria-Gloria
通讯作者：
Basanez, Maria-Gloria
DOI：
10.1111/rssa.12069
发表时间：
2015-02-01
影响因子：
2
作者：
Giorgi, Emanuele;Sesay, Sanie S. S.;Diggle, Peter J.
通讯作者：
Diggle, Peter J.
DOI：
10.1080/01621459.2015.1123158
发表时间：
2016-09-01
影响因子：
3.7
作者：
Diggle, Peter J.;Giorgi, Emanuele
通讯作者：
Giorgi, Emanuele
影响因子：
2
作者：
Giorgi, Emanuele;Diggle, Peter J.;Noor, Abdisalan M.
通讯作者：
Noor, Abdisalan M.