A tutorial on the case time series design for small-area analysis | BMC Medical Research Methodology | Full Text

The increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels. This tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient c

A tutorial on the case time series design for small-area analysis Research Open access Published: 30 April 2022 Volume 22 , article number 129 ( 2022 ) Cite this article You have full access to this open access article Download PDF Save article View saved research BMC Medical Research Methodology Aims and scope Submit manuscript A tutorial on the case time series design for small-area analysis Download PDF Abstract Background The increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of e

related reading

• The Case Time Series Designncbi.nlm.nih.gov
• Introduction to Time Series Analysis — Time Series Analysis Handbookphdinds-aim.github.io
• The Unreasonable Difficulty of Time Series Forecastingsuzyahyah.github.io
• Step-by-Step Diffusion: An Elementary Tutorialarxiv.org
• Time Series - From Analyzing the Past to Predicting the Future | Towards Data Sciencetowardsdatascience.com
• Hierarchical Modelingbetanalpha.github.io
• Time Series Clustering - Deriving Trends and Archetypes from Sequential Data | Towards Data Sciencetowardsdatascience.com
• 10 Complex Diff-in-Diff Designs – Causal Inference<br/>*The Remix*mixtape.scunning.com
• Analysis of Observational Self-matched Data to Examine Acute Triggers of Outcome Events with Abrupt Onset - PMCncbi.nlm.nih.gov
• 8 Panel Data – <span style='font-weight: 700'>Causal Inference</span><br/> <i style='color: #00b7ff'>The Mixtape</i>mixtape.scunning.com
• Modeling the uncertainty on the covariance matrix for probabilistic forecast reconciliationarxiv.org
• A Review of Modern Stochastic Modeling: SDE/SPDE Numerics, Data-Driven Identification, and Generative Methods with Applications in Biology and Epidemiologyarxiv.org