Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland

Mezzetti, Maura

doi:10.1007/s10260-011-0177-9

Cited by 14 publications

(11 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous work has developed Kronecker‐structured covariance matrices of 2 or 3 components. Repeated measures are the most common application of these methods, as well as applications to spatial and imaging data. Additional structure may be imposed on the subblocks, such as circulant or Toeplitz .…”

Section: Introductionmentioning

confidence: 99%

Separable covariance models for health care quality measures across years and topics

Hatfield

Zaslavsky

2018

Statistics in Medicine

View full text Add to dashboard Cite

Public quality reports for Medicare Advantage health plans include 11 measures of patient experiences reported in the annual Consumer Assessment of Healthcare Providers and Systems surveys. Computing summaries at the health plan level (of multiple measures in multiple years) yields an array-structured random variable. To summarize associations among measures and years, we model the variance-covariance matrix governing the plan-level vectors of yearly quality measures as a Kronecker product of an across-measure matrix and an across-year matrix, or a sum of such Kronecker products. This approach extends separable covariance structure to Fay-Herriot models. In addition, we develop linear combinations of Kronecker products similar to principal components for array random variables. To each Kronecker-product term, we apply post hoc analyses suited to the corresponding dimension of the cross-classification: 1-way factor analysis for the across-measure factor and time-series analysis to the across-year factor. These methods draw out key patterns of variation in the quality measures over time and suggest new strategies for reporting quality information to consumers.

show abstract

Section: Introductionmentioning

confidence: 99%

Separable covariance models for health care quality measures across years and topics

Hatfield

Zaslavsky

2018

Statistics in Medicine

View full text Add to dashboard Cite

show abstract

“…Can Bayesian methods be used for factor analysis? Yes: (e.g., Arminger & Muthén, 1998;Ghosh & Dunson, 2009;Merkle & Rosseel, 2016;Mezzetti, 2012;Song & Lee, 2001. Can Bayesian methods be applied to item response theory?…”

Section: Bayesian Analysis Applies To Any Parameterized Model Of Datamentioning

confidence: 99%

Bayesian data analysis for newcomers

Kruschke¹,

Liddell²

2017

Preprint

View full text Add to dashboard Cite

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented, that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

show abstract

“…A spatio-temporal mixture model was proposed to analyse the space-time variation in respiratory cancers in the state of South Carolina [24]. [25] proposed a hierarchical Bayesian factor model for spatially correlated data to explain across and within county correlations of cancer incidence rates by assuming that all different cancer types (12 for females and 10 for males) share one or more spatially correlated common factors. The model was to age-standardised cancer incidence rates by sex in 56 counties of Scotland.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics

Jahan

Duncan

Cramb

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Cancer atlases often provide estimates of cancer incidence, mortality or survival across small areas of a region or country. A recent example of a cancer atlas is the Australian cancer atlas (ACA), that provides interactive maps to visualise spatially smoothed estimates of cancer incidence and survival for 20 different cancers over 2148 small areas across Australia. Methods: The present study proposes a multivariate Bayesian meta-analysis model, which can model multiple cancers jointly using summary measures without requiring access to the unit record data. This new approach is illustrated by modelling the publicly available spatially smoothed standard incidence ratios (SIR) for multiple cancers in the ACA divided into three groups: common, rare/less common and smoking-related. The multivariate Bayesian meta-analysis models are fitted to each group in order to explore any possible association between the cancers in three remoteness regions: major cities, regional and remote areas across Australia. Results: Substantive correlation was observed among some cancer types. There was evidence that the magnitude of this correlation varied according to remoteness of a region. High risk areas for specific combinations of cancer types were identified and visualised from the proposed model. Conclusions: Publicly available spatially smoothed disease estimates can be used to explore additional research questions by modelling multiple cancer types jointly. These proposed multivariate meta-analysis models could be useful when unit record data are unavailable because of privacy and confidentiality requirements.

show abstract

Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland

Cited by 14 publications

References 46 publications

Separable covariance models for health care quality measures across years and topics

Separable covariance models for health care quality measures across years and topics

Bayesian data analysis for newcomers

Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics

Contact Info

Product

Resources

About