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.
Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4001, Australia.
Int J Health Geogr. 2020 Oct 17;19(1):42. doi: 10.1186/s12942-020-00234-0.
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.
癌症图谱通常提供对一个地区或国家小区域内癌症发病率、死亡率或生存率的估计。最近的一个癌症图谱示例是澳大利亚癌症图谱（ACA），它提供了交互式地图，用于可视化澳大利亚 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值。
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指标对多个癌症进行联合建模，而无需访问单位记录数据。通过对 ACA 中三种不同分组（常见、罕见/较少见和与吸烟相关）的多个癌症的空间平滑标准化发病率比进行建模，说明了这种新方法。为了探索澳大利亚三个偏远地区（主要城市、区域和偏远地区）中癌症之间的任何可能关联，对每个组中的多变量贝叶斯荟萃分析模型进行拟合。通过计算每个区域中每个癌症组的后相关矩阵，检查了每组中包含的癌症之间的相关性。使用 Jennrich 相等相关矩阵检验（Jennrich 在 J Am Stat Assoc. 1970;65(330):904-12. https://doi.org/10.1080/01621459.1970.10481133 ）比较了不同偏远地区的后相关矩阵。
观察到一些癌症类型之间存在实质性相关性。有证据表明，这种相关性的大小根据区域的偏远程度而有所不同。例如，在主要城市中，前列腺癌和肺癌之间存在显著的负相关，但在同一对癌症类型的区域和偏远地区则没有相关性。从提出的模型中确定并可视化了特定癌症类型组合的高风险区域。
可以使用公开的空间平滑疾病估计值，通过联合建模多个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，这些提出的多变量荟萃分析模型可能会很有用。
