Logo image
Back
Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics
Journal article   Open access   Peer reviewed

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

Farzana Jahan, Earl W. Duncan, Susana M Cramb, Peter D. Baade and Kerrie L. Mengersen
International journal of health geographics, Vol.19(1), Art. 42
2020
DOI: https://doi.org/10.1186/s12942-020-00234-0
PMID: 33069256
pdf
Published5.88 MBDownloadView
Open Access

Abstract

Australia - epidemiology Bayes Theorem Humans Incidence Male Neoplasms - diagnosis Neoplasms - epidemiology Smoking Spatial statistics
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

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Metrics

8 File views/ downloads
86 Record Views
7 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
Citation topics
9 Mathematics
9.92 Statistical Methods
9.92.220 Robust Estimation
Web Of Science research areas
Public, Environmental & Occupational Health
ESI research areas
Social Sciences, general
Logo image