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Open AccessArticle

Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

by 1,* and 1,2
1
Department of Quantitative Methods, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
2
TiDES Institute, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Manuel Alberto M. Ferreira
Mathematics 2021, 9(4), 299; https://doi.org/10.3390/math9040299
Received: 30 December 2020 / Revised: 27 January 2021 / Accepted: 30 January 2021 / Published: 3 February 2021
(This article belongs to the Special Issue Quantitative Methods in Health Care Decisions)
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. View Full-Text
Keywords: interrupted time series analysis; generalized additive models; simulation analysis; pharmaceutical prescriptions; Spain interrupted time series analysis; generalized additive models; simulation analysis; pharmaceutical prescriptions; Spain
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MDPI and ACS Style

Pinilla, J.; Negrín, M. Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions. Mathematics 2021, 9, 299. https://doi.org/10.3390/math9040299

AMA Style

Pinilla J, Negrín M. Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions. Mathematics. 2021; 9(4):299. https://doi.org/10.3390/math9040299

Chicago/Turabian Style

Pinilla, Jaime; Negrín, Miguel. 2021. "Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions" Mathematics 9, no. 4: 299. https://doi.org/10.3390/math9040299

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