e.g. mhealth
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Skip search results from other journals and go to results- 5 JMIR Public Health and Surveillance
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Therefore, we conducted a Bayesian meta-analysis instead of the conventional frequentist meta-analysis to improve the precision of the pooled effect size estimation and to provide the probability of the intervention effect being >0 [22-24].
The protocol for this review was registered in PROSPERO (registration number CRD42021270856).
J Med Internet Res 2025;27:e63111
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We will use Bayesian network analysis to generate a directed acyclic graph, which probabilistically describes the trajectory of symptoms, care, and outcomes for IETP participants. This network will enable us to explore the contribution of the baseline phenotype to the decision to use a particular treatment course and the contribution of those treatments to the participants’ resulting outcome measures [53].
JMIR Res Protoc 2023;12:e44776
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Bayesian spatiotemporal models have received substantial attention in the past several years in opioid-related research, given their ability to include temporal and spatial correlations and improved precision in small area estimation [20-22]. Sumetsky et al [20] for instance, developed a Bayesian logistic growth model for opioid overdose mortality predictions for 146 counties in North and South Carolina.
JMIR Public Health Surveill 2023;9:e41450
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The age- and sex-stratified associations between vegetation and mental health disorders were analyzed using the Bayesian spatial hierarchical modeling technique. In the Bayesian spatial hierarchical models, the observed counts, Oi, of the combined mental health disorder in each neighborhood i (where i=1,2,…140) were assumed to follow a Poisson distribution (Oi ~ Poisson(λi)). Here, λi denotes the expected number of mental health disorder cases in the neighborhood i.
JMIR Public Health Surveill 2022;8(7):e34782
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Unbalanced Risk of Pulmonary Tuberculosis in China at the Subnational Scale: Spatiotemporal Analysis
A Bayesian spatiotemporal model was built to analyze spatiotemporal patterns of PTB notification rates from 2009 to 2018. The spatiotemporal process of the PTB notification rate was decomposed into three components: the spatial random effect, overall time trend, and spatiotemporal interaction effect [13-15]. A Poisson regression model connected by a logarithmic function was used to model the process based on count data [16,17].
JMIR Public Health Surveill 2022;8(7):e36242
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We adapted a Bayesian hierarchical model to estimate and extrapolate hospitalization rates, accounting for uncertainty and variability between states and across time.
We used COVID-19 hospitalization data from COVID-NET. The network identifies hospitalized patients with a positive SARS-Co V-2 test, including molecular assay and antigen detection, during hospitalization or within 14 days prior to hospitalization [9].
JMIR Public Health Surveill 2022;8(6):e34296
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In addition to the prespecified analyses, unplanned Bayesian analyses were performed. The higher than anticipated attrition rate underpowered the planned null hypothesis tests, and the Bayesian analyses were included to calculate the probability that the intervention had an effect on smoking outcomes.
JMIR Mhealth Uhealth 2021;9(10):e29913
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Db-efficient design (b stands for Bayesian): A Bayesian approach is whereby the parameter is not known with certainty but may be described by its probability distribution.
The best practice is to pilot the DCE. For the pilot phase, there is limited information available and using the Dz-efficient or Dp-efficient design is sensible.
J Med Internet Res 2021;23(10):e32365
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We considered the following Bayesian spatiotemporal model [18,19]. Let Yit denote the number of confirmed cases from census tract i and week t; we assumed a negative binomial distribution with incidence risk μit (ie, Yit|μit ~ NB(Niμit), with Nit the population size as the offset.
JMIR Public Health Surveill 2021;7(8):e29205
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Prior research has demonstrated the potential utility of Bayesian inference [8,9] and machine learning [10,11] methods in diagnostic decision making, but computational clinical decision support has often been underutilized due to a lack of accessibility, transparency, workflow integration, and most importantly, the flexibility to incorporate local provider beliefs into the diagnostic model [12,13].
J Med Internet Res 2020;22(12):e24478
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