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

Farzana Jahan1, Earl W Duncan2, Susana M Cramb3

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This study introduces a new statistical model to analyze multiple cancer types together using existing data, revealing varying correlations between cancers based on geographic remoteness in Australia.

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Area of Science:

  • Biostatistics
  • Spatial Epidemiology
  • Public Health

Background:

  • Cancer atlases map disease incidence, mortality, or survival across geographic areas.
  • The Australian Cancer Atlas (ACA) provides spatially smoothed estimates for 20 cancer types across Australia.
  • Existing methods often require unit-level data, which can be restricted due to privacy concerns.

Purpose of the Study:

  • To propose a multivariate Bayesian meta-analysis model for joint analysis of multiple cancer types.
  • To utilize publicly available summary measures from the ACA without needing unit-record data.
  • To explore associations between cancer types across different Australian remoteness regions.

Main Methods:

  • A multivariate Bayesian meta-analysis model was developed to jointly model multiple cancers.
  • The model was applied to spatially smoothed standardized incidence ratios from the ACA, grouped by cancer type (common, rare, smoking-related).
  • Posterior correlation matrices were computed for cancer pairs within major cities, regional, and remote areas, and compared using Jennrich's test.

Main Results:

  • Significant correlations were found between certain cancer types.
  • The magnitude of these correlations varied by region's remoteness.
  • An example showed a negative correlation between prostate and lung cancer in major cities, contrasting with zero correlation in regional/remote areas.

Conclusions:

  • Publicly available disease estimates can support joint modeling of multiple cancer types.
  • The proposed multivariate meta-analysis models offer a valuable approach when unit-record data are inaccessible.
  • This method can identify high-risk areas for specific cancer combinations and inform public health strategies.