Journal article
Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics
International journal of health geographics, Vol.19(1), Art. 42
2020
PMID: 33069256
Abstract
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 cancer types 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 standardised incidence ratios 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. The correlation between the pairs of cancers included in each multivariate model for a group was examined by computing the posterior correlation matrix for each cancer group in each region. The posterior correlation matrices in different remoteness regions were compared using Jennrich’s test of equality of correlation matrices (Jennrich in J Am Stat Assoc. 1970;65(330):904–12. https://doi.org/10.1080/01621459.1970.10481133).
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. For example, there has been significant negative correlation between prostate and lung cancer in major cities, but zero correlation found in regional and remote areas for the same pair of cancer types. 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.
Details
- Title
- Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics
- Authors/Creators
- Farzana Jahan (Author)Earl W. Duncan (Author) - ARC Centre of Excellence in Mathematical and Statistical Frontiers, School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD 4001, AustraliaSusana M Cramb (Author) - Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4001, AustraliaPeter D. Baade (Author) - Cancer Council Queensland, 553 Gregory Terrace, Fortitude Valley, Brisbane, QLD 4006, AustraliaKerrie L. Mengersen (Author) - ARC Centre of Excellence in Mathematical and Statistical Frontiers, School of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD 4001, Australia
- Publication Details
- International journal of health geographics, Vol.19(1), Art. 42
- Publisher
- BioMed Central Ltd as Part of Springer Nature.
- Grants
- Bayesian learning for decision making in the big data era, 2019-06-14T18:04:43.803493, Australian Research Council (Australia, Canberra) - ARC
- Identifiers
- 991005575768307891
- Copyright
- © 2020 The Author(s).
- Murdoch Affiliation
- College of Science, Technology, Engineering and Mathematics
- Language
- English
- Resource Type
- Journal article
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