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. 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 ). 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. 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.

中文翻译：

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多变量贝叶斯荟萃分析：使用汇总统计对多种癌症类型进行联合建模


癌症地图集通常提供一个地区或国家小范围内癌症发病率、死亡率或生存率的估计。癌症地图集的最新示例是澳大利亚癌症地图集 (ACA)，它提供交互式地图，以可视化对澳大利亚 2148 个小区域内 20 种不同癌症类型的癌症发病率和生存率的空间平滑估计。本研究提出了一种多变量贝叶斯荟萃分析模型，该模型可以使用汇总措施联合对多种癌症进行建模，而无需访问单位记录数据。这种新方法通过对 ACA 中多种癌症的公开空间平滑标准化发病率进行建模来说明，该发病率分为三组：常见、罕见/不太常见和吸烟相关。多变量贝叶斯荟萃分析模型适用于每个组，以探索三个偏远地区（澳大利亚的主要城市、偏远地区和偏远地区）癌症之间可能的关联。通过计算每个区域中每个癌症组的后验相关矩阵来检查组的每个多变量模型中包含的癌症对之间的相关性。使用 Jennrich 相关矩阵相等性检验比较不同偏远地区的后验相关矩阵（Jennrich in J Am Stat Assoc. 1970;65(330):904–12。https://doi.org/10.1080/01621459.1970.10481133 ）。在一些癌症类型之间观察到了实质性相关性。有证据表明，这种相关性的大小根据地区的偏远程度而变化。 例如，在大城市中，前列腺癌和肺癌之间存在显着的负相关性，但在地区和偏远地区，同一对癌症类型的相关性为零。从所提出的模型中识别并可视化了特定癌症类型组合的高风险区域。公开的空间平滑疾病估计可用于通过联合建模多种癌症类型来探索其他研究问题。当由于隐私和保密要求而无法获得单位记录数据时，这些提出的多元荟萃分析模型可能会很有用。