Statistical Methods for Policy-Relevant Questions in Health and Criminology

Whalen, Mena

doi:https://doi.org/10.21985/n2-83ep-mv36

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Statistical Methods for Policy-Relevant Questions in Health and Criminology

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The logistics of policy implementation can lead to a delay from when the actual change in behavior occurs, leading to a shift in a time series. Using change point analysis allows for the data to determine where a change in mean, or other parameters, occurred. But when policy is implemented across multiple locations, how can a researcher understand where change points are occurring at across all locations? Can those locations be grouped together based on their change point? We propose a methodology for clustering panels of nonlinear time series and develop diagnostics to assess the clustering. The change point component of the methodology allows for trends and point anomalies to be detected for each time series. This methodology incorporates spatial and demographic information from the locations into the clustering aspect of the methodology. In a practical application of our methodology, we investigate when average counts of emergency department (ED) visits change related to when the Affordable Care Act was enacted, using monthly time series from 88 locations. Using the diagnostic measures developed and innovative data analysis techniques we understand the groupings of these locations and where in time these groups were changing. In another data application we investigated the impact COVID-19 had on crime rates in the city of Chicago. Using our methodology and data visualization tools, we examined if neighborhoods experience a reduction in crime through their change points and how to group these time series together. This paper also explores the use of Gaussian graphical models to understand metabolic networks to assist in the development of new targeted assays. A metabolite can be measured through a well-developed panel, called a targeted assay, or through a mass spectrometer reading. The mass spectrometer measure, an untargeted panel, is poorly measured but can detect all metabolites present in the sample unlike the targeted panel which only measures these few well-studied metabolites. Given the high cost of targeting a metabolite, it is important to investigate the benefits of a possible addition of a metabolite to a targeted panel. We developed a model based on the determination of successful targeting of a metabolite using variables related to the metabolite in the network.

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