Using mortality to predict incidence for rare and lethal cancers in very small areas

Etxeberria, Jaione; Goicoa, T.; Ugarte, M. D.

doi:10.1002/bimj.202200017

Cited by 5 publications

(4 citation statements)

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“…Etxeberria et al. (2023) predict incidence rates for rare and lethal cancers by borrowing strength from mortality data using spatiotemporal models with shared spatial and age components. Different extensions of age‐period‐cohort models including spatial random effects have also been proposed for the prediction of cancer mortality and incidence data (see Lagazio et al., 2003; Papoila et al., 2014; Schmid & Held, 2004; or Etxeberria et al., 2017 among others).…”

Section: Introductionmentioning

confidence: 99%

“…In Corpas-Burgos and Martinez-Beneito (2021), an enhancement of a previous autoregressive (AR) spatiotemporal model proposed by Martínez-Beneito et al (2008) was considered for 5-year ahead forecasting of different cancer site mortality data in the 540 municipalities of the Valencian autonomous region of Spain. Etxeberria et al (2023) predict incidence rates for rare and lethal cancers by borrowing strength from mortality data using spatiotemporal models with shared spatial and age components. Different extensions of age-period-cohort models including spatial random effects have also been proposed for the prediction of cancer mortality and incidence data (see Lagazio et al, 2003;Papoila et al, 2014;Schmid & Held, 2004;or Etxeberria et al, 2017 among others).…”

Section: Introductionmentioning

confidence: 99%

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A scalable approach for short‐term disease forecasting in high spatial resolution areal data

Orozco‐Acosta,

Riebler,

Adin

et al. 2023

Biometrical J

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Short‐term disease forecasting at specific discrete spatial resolutions has become a high‐impact decision‐support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatiotemporal models. The purpose of this paper is to provide a method for short‐term predictions in high‐dimensional areal data based on a newly proposed “divide‐and‐conquer” approach. We assess the predictive performance of this method and other classical spatiotemporal models in a validation study that uses cancer mortality data for the 7907 municipalities of continental Spain. The new proposal outperforms traditional models in terms of mean absolute error, root mean square error, and interval score when forecasting cancer mortality 1, 2, and 3 years ahead. Models are implemented in a fully Bayesian framework using the well‐known integrated nested Laplace estimation technique.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A scalable approach for short‐term disease forecasting in high spatial resolution areal data

Orozco‐Acosta,

Riebler,

Adin

et al. 2023

Biometrical J

Self Cite

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“…In Corpas-Burgos and Martinez-Beneito ( 2021), an enhancement of a previous autoregressive (AR) spatio-temporal model proposed by Martínez-Beneito et al (2008) was considered for five-year ahead forecasting of different cancer site mortality data in the 540 municipalities of the Valencian autonomous region of Spain. Etxeberria et al (2023) predict incidence rates for rare and lethal cancers by borrowing strength from mortality data using spatiotemporal models with shared spatial and age components. Different extensions of APC (age-period-cohort) models including spatial random effects have been also proposed for the prediction of cancer mortality and incidence data (see Lagazio et al, 2003;Schmid and Held, 2004;Papoila et al, 2014or Etxeberria et al, 2017.…”

Section: Introductionmentioning

confidence: 99%

“…Forecasting the risk or rate of diseases in a spatio-temporal context has numerous applications, including health resource planning and allocation, as well as the prioritization of prevention policies, among other areas (Corpas-Burgos and Martinez-Beneito, 2021). In Spain, updated registries of cancer mortality data in small areas (for example, municipalities) have delays of around 3 years (Etxeberria et al, 2023). Therefore, developing reliable methods to complete unavailable data is very important.…”

Section: Introductionmentioning

confidence: 99%

Exploring disease mapping models in big data contexts: some new proposals

Orozco Acosta

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Disease mapping is a highly relevant and significant research area within the field of spatial statistics (areal data), as it offers invaluable support for public health decision-making. Due to the high variability of classical risk estimators, such as the standardized mortality ratio (SMR), the use of statistical models becomes essential to obtain a more consistent representation of the underlying disease risk. During the last decades, several statistical models have been proposed in the disease mapping literature for smoothing risks in space and time, most of them extending the seminal work of Besag et al. (1991) based on conditional autoregressive (CAR) priors. However, the scalability of these models, specifically their utility in scenarios where the number of small areas increases significantly, has not been extensively studied. Thus, the main purpose of this dissertation is to propose new scalable Bayesian modelling methods to smooth incidence/mortality risks (or rates) in high-dimensional spatial and spatio-temporal areal data based on the “divide-and-conquer” approach. The current dissertation is developed with the following main objectives. The first objective is to review the literature about the main contributions of spatial and spatio-temporal disease mapping that are relevant to the research goals. Chapter 1 provides a general overview of model fitting and inference focusing on the widely used integrated nested Laplace approximation (INLA) technique for latent Gaussian models within the Bayesian paradigm (Rue et al., 2009). The chapter also covers the description of how to compute approximations of model selection criteria based on the deviance and the predictive distribution under our scalable model proposals. A brief description of the R package bigDM is also included, which implements all the algorithms and models proposed in this dissertation. The second objective of this dissertation is to propose a scalable Bayesian modelling method for handling high-dimensional spatial count data. In Chapter 2, we provide a comprehensive description of our novel risk smoothing method. We also conduct a multi-scenario simulation study involving nearly 8000 Spanish municipalities to compare our proposed method with the well-known CAR models in terms of goodness of fit and risk estimation accuracy. Additionally, we illustrate the behaviour of the scalable models by analysing male colorectal cancer mortality data from Spanish municipalities using two different partition strategies of the spatial domain. The third objective is to extend our scalable Bayesian modelling approach for smoothing mortality or incidence risks to analyze high-dimensional spatio-temporal count data. In Chapter 3, we present a comprehensive description of the spatiotemporal CAR models originally proposed by Knorr-Held (2000), which are the basis of our new modelling proposal for analyzing spatio-temporal areal data. The chapter also explains the parallel and distributed strategies implemented in the bigDM package to speed up computations by using the R package future (Bengtsson, 2021). A simulation study is conducted to compare our new scalable proposal with two different merging strategies against traditional spatio-temporal CAR models using the map of the Spanish municipalities as a template. Additionally, we evaluate our proposal in terms of computational time. Finally, we illustrate and compare all the approaches described in this chapter by analyzing the spatio-temporal evolution for male lung cancer mortality data in Spanish continental municipalities during the period 1991-2015. The fourth objective is to assess the suitability of the method developed in Chapter 3 for short-term forecasting in high spatial resolution data. In Chapter 4, we present the spatio-temporal CAR model, which incorporates missing observations in the response variable for the time periods to be forecasted. Additionally, a validation study is conducted to assess the predictive ability of the models for one, two and three periods ahead forecasting using real lung cancer mortality data in Spanish municipalities. In this chapter, we also compare the predictive performance of the models using scoring rules based on leave-one-out and leave-group-out cross-validation strategies (Liu and Rue, 2022). The fifth objective is transversal to all chapters. The aim was to develop an open-source R language package named bigDM (Adin et al., 2023b) that consolidates all the methods proposed in this dissertation making them readily available for use by the scientific community. The dissertation ends with the main conclusions and future research lines.

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Multivariate Disease Mapping Models to Uncover Hidden Relationships Between Different Cancer Sites

Adin

Goicoa

Ugarte

2023

Statistical Methods at the Forefront of Biomedical Advances

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Using mortality to predict incidence for rare and lethal cancers in very small areas

Cited by 5 publications

References 45 publications

A scalable approach for short‐term disease forecasting in high spatial resolution areal data

A scalable approach for short‐term disease forecasting in high spatial resolution areal data

Exploring disease mapping models in big data contexts: some new proposals

Multivariate Disease Mapping Models to Uncover Hidden Relationships Between Different Cancer Sites

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