Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/106242
Title: Non-parametric generalized additive models as a tool for evaluating policy interventions
Authors: Pinilla Domínguez, Jaime 
Negrín Hernández, Miguel Ángel 
UNESCO Clasification: 530202 Modelos econométricos
5904 Instituciones políticas
Keywords: Generalized Additive Models
Interrupted Time Series Analysis
Pharmaceutical Prescriptions
Simulation Analysis
Spain
Issue Date: 2021
Journal: Mathematics 
Abstract: The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.
URI: http://hdl.handle.net/10553/106242
ISSN: 2227-7390
DOI: 10.3390/math9040299
Source: Mathematics [EISSN 2227-7390], v. 9 (4), 299, (Febrero 2021)
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