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Lognormal structured additive regression model for spatio-temporal data and its application to breast cancer data in Iran | ||
| Journal of Data Science and Modeling | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 12 مرداد 1405 اصل مقاله (573.06 K) | ||
| نوع مقاله: original | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2026.88625.1079 | ||
| نویسندگان | ||
| Soudabe Sajjadipanah* 1؛ Hasti Hashemi2؛ Seyyed mahmoud Mirjalili3 | ||
| 1staff of Boushehr Medical University | ||
| 2Department of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 3Department of Statistics, Velayat University, Velayat, Iran | ||
| چکیده | ||
| Medical data are typically recorded across geographic regions and over successive time points, giving rise to spatio-temporal structures that present important statistical modelling challenges. In this study, we propose a spatio-temporal regression model based on the lognormal distribution within the structured additive regression framework. The lognormal assumption accommodates the positive skewness and heteroscedasticity of the incidence rate data. Bayesian inference for the model parameters was conducted using the integrated nested Laplace approximation (INLA), which delivers substantial computational gains over Markov chain Monte Carlo methods while maintaining comparable accuracy. We systematically compared four types of spatio-temporal interactions (Types I–IV) to identify the most parsimonious and best-fitting dependence structure. To evaluate performance, we applied the proposed model and several competitors to breast cancer incidence data from all provinces of Iran over the period 2010–2019. The results demonstrated the clear superiority of the lognormal structured additive regression (LNSTAR) model, with the Type II (temporal-only) interaction providing the best fit. Beyond its methodological contribution, this analysis provides actionable risk stratification to inform targeted screening policies in Iran. | ||
| کلیدواژهها | ||
| Bayesian inference؛ Breast cancer؛ Integrated nested Laplace approximation؛ Lognormal structured additive regression model؛ Spatio-temporal interaction | ||
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