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

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 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.

Data combining using mixtures of g-priors with application on county-level female breast cancer prevalence

“…One of the most popular data syntheses tool is meta-analysis (Brockwell and Gordon, 2001;Burke et al, 2017;Jackson et al, 2011;Riley et al, 2007Riley et al, , 2008 and a typical task is the inference for an overall effect or quantifying variabilities within or across multiple data sources. It has widespread applications in many fields including clinical trails (Moreno et al, 2018;Verde et al, 2016), psychology (Williams et al, 2018), medical science (Jahan et al, 2020;Lin and Chu, 2018), etc. Bayesian meta-analysis receives tremendous attention due to its sound performance in some challenging situations such as a small heterogeneity across studies (Chung et al, 2013;Hong et al, 2021) and incomplete outcomes from some data sources (Wei and Higgins, 2013).…”