Spatial Statistical Models: An Overview under the Bayesian Approach

Louzada, Francisco; Nascimento, Dejair Caitano do; Egbon, Osafu Augustine

doi:10.3390/axioms10040307

Cited by 16 publications

(8 citation statements)

References 136 publications

(196 reference statements)

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“…Future studies are necessary to explore random effects related to personal characteristics to promote a broader knowledge involving causality on dynamic entropy data. Moreover, each person shows a particular baseline towards the central (spatial) forcedisplacement, and a more complex statistical structure can be accommodated, such as spatial-domain in this data-driven modeling [49].…”

Section: Discussionmentioning

confidence: 99%

High Definition tDCS Effect on Postural Control in Healthy Individuals: Entropy Analysis of a Crossover Clinical Trial

et al. 2022

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Objective: Converging evidence supporting an effect of transcranial direct current stimulation (tDCS) on postural control and human verticality perception highlights this strategy as promising for post-stroke rehabilitation. We have previously demonstrated polarity-dependent effects of high-definition tDCS (HD-tDCS) on weight-bearing asymmetry. However, there is no investigation regarding the time-course of effects on postural control induced by HD-tDCS protocols. Thus, we performed a nonlinear time series analysis focusing on the entropy of the ground reaction force as a secondary investigation of our randomized, double-blind, placebo-controlled, crossover clinical trial. Materials and Methods: Twenty healthy right-handed young adults received the following conditions (random order, separate days); anode center HD-tDCS, cathode center HD-tDCS or sham HD-tDCS at 1, 2, and 3 mA over the right temporo-parietal junction (TPJ). Using summarized time series of transfer entropy, we evaluated the exchanging information (causal direction) between both force plates and compared the dose-response across the healthy subjects with a Generalized Linear Hierarchical/Mixed Model (GLMM). Results: We found significant variation during the dynamic information flow (p < 0.001) among the dominant bodyside (and across time). A greater force transfer entropy was observed from the right to the left side during the cathode-center HD-tDCS up to 2 mA, with a causal relationship in the information flow (equilibrium force transfer) from right to left that decreased over time. Conclusions: HD-tDCS intervention induced a dynamic influence over time on postural control entropy. Right hemisphere TPJ stimulation using cathode-center HD-tDCS can induce an asymmetry of body weight distribution towards the ipsilateral side of stimulation. These results support the clinical potential of HD-tDCS for post-stroke rehabilitation.

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Section: Discussionmentioning

confidence: 99%

High Definition tDCS Effect on Postural Control in Healthy Individuals: Entropy Analysis of a Crossover Clinical Trial

et al. 2022

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“…However, determining how to most effectively borrow historical information in a Bayesian setting is an open question, as incorrectly borrowing (ie, when the historical and current data are in disagreement) can result in inaccurate statistical inference, while failing to utilize supporting information is inefficient. To this end, several methods that allow for data‐driven borrowing of historical information to improve statistical inference in the current data analysis have been introduced (eg, References 1‐3) and are becoming increasingly used in practice across many different scientific disciplines (eg, References 4‐10). We detail several of these major methodological categories in Section 3.…”

Section: Introductionmentioning

confidence: 99%

A scaled kernel density estimation prior for dynamic borrowing of historical information with application to clinical trial design

Warren,

Wang,

Ciarleglio

2024

Statistics in Medicine

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Incorporating historical data into a current data analysis can improve estimation of parameters shared across both datasets and increase the power to detect associations of interest while reducing the time and cost of new data collection. Several methods for prior distribution elicitation have been introduced to allow for the data‐driven borrowing of historical information within a Bayesian analysis of the current data. We propose scaled Gaussian kernel density estimation (SGKDE) prior distributions as potentially more flexible alternatives. SGKDE priors directly use posterior samples collected from a historical data analysis to approximate probability density functions, whose variances depend on the degree of similarity between the historical and current datasets, which are used as prior distributions in the current data analysis. We compare the performances of the SGKDE priors with some existing approaches using a simulation study. Data from a recently completed phase III clinical trial of a maternal vaccine for respiratory syncytial virus are used to further explore the properties of SGKDE priors when designing a new clinical trial while incorporating historical data. Overall, both studies suggest that the new approach results in improved parameter estimation and power in the current data analysis compared to the considered existing methods.

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“…However, SIR and SMR have limitations when the outcome is rare or when the population is small [ 10 ]. To overcome these issues, Bayesian spatial models are applied to obtain smoothed risk by considering spatial dependence (structured and unstructured spatial random effects) in the model [ 11 , 12 ]. Any overdispersion or spatial dependency in the data that cannot be accounted for by the covariates is taken into account by the random effects in the model [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

Tesema

Tessema

Héritier

et al. 2023

IJERPH

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With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.

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Spatial Statistical Models: An Overview under the Bayesian Approach

Cited by 16 publications

References 136 publications

High Definition tDCS Effect on Postural Control in Healthy Individuals: Entropy Analysis of a Crossover Clinical Trial

High Definition tDCS Effect on Postural Control in Healthy Individuals: Entropy Analysis of a Crossover Clinical Trial

A scaled kernel density estimation prior for dynamic borrowing of historical information with application to clinical trial design

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

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