[论文解读] A Spatio-Temporal Multivariate Shared Component Model with an Application in Iran Cancer Data

Behzad Mahaki, Yadollah Mehrabi+2 more|PubMed|Jul 19, 2017

Health Systems, Economic Evaluations, Quality of LifeEconomics, Econometrics and Finance参考文献 29被引用 19

一句话总结

本文提出了一种时空多变量共享成分模型，联合分析2005至2009年间伊朗七种癌症的发病率，整合共享潜在风险成分中的空间与时间趋势。该模型识别出显著的地理与时间模式，揭示食道癌和胃癌存在强烈的空间聚集性，并凸显了乳腺癌与前列腺癌等疾病对之间的共享风险因素。

ABSTRACT

Background: Among the proposals for joint disease mapping, the shared component model has become more popular. Another advance to strengthen inference of disease data is the extension of purely spatial models to include time aspect. We aim to combine the idea of multivariate shared components with spatio-temporal modelling in a joint disease mapping model and apply it for incidence rates of seven prevalent cancers in Iran which together account for approximately 50% of all cancers. Methods: In the proposed model, each component is shared by different subsets of diseases, spatial and temporal trends are considered for each component, and the relative weight of these trends for each component for each relevant disease can be estimated. Results: For esophagus and stomach cancers the Northern provinces was the area of high risk. For colorectal cancer Gilan, Semnan, Fars, Isfahan, Yazd and East-Azerbaijan were the highest risk provinces. For bladder and lung cancer, the northwest were the highest risk area. For prostate and breast cancers, Isfahan, Yazd, Fars, Tehran, Semnan, Mazandaran and Khorasane-Razavi were the highest risk part. The smoking component, shared by esophagus, stomach, bladder and lung, had more effect in Gilan, Mazandaran, Chaharmahal and Bakhtiari, Kohgilouyeh and Boyerahmad, Ardebil and Tehran provinces, in turn. For overweight and obesity component, shared by esophagus, colorectal, prostate and breast cancers the largest effect was found for Tehran, Khorasane-Razavi, Semnan, Yazd, Isfahan, Fars, Mazandaran and Gilan, in turn. For low physical activity component, shared by colorectal and breast cancers North-Khorasan, Ardebil, Golestan, Ilam, Khorasane-Razavi and South-Khorasan had the largest effects, in turn. The smoking component is significantly more important for stomach than for esophagus, bladder and lung. The overweight and obesity had significantly more effect for colorectal than of esophagus cancer. Conclusions: The presented model is a valuable model to model geographical and temporal variation among diseases and has some interesting potential features and benefits over other joint models.

研究动机与目标

• 开发一种联合建模框架，整合时空动态与多变量疾病映射，以改进对癌症发病率的推断。
• 识别解释伊朗多种癌症类型中空间与时间变异的共享潜在风险成分。
• 通过共享成分在相关疾病间借用信息，提高对发病率较低癌症的估计精度。
• 探索超越单变量或纯空间模型的癌症发病率地理与时间模式。
• 提供一种灵活的贝叶斯分层模型，能够估计每个共享成分在时间和空间上对个体癌症的相对贡献。

提出的方法

• 提出一种贝叶斯分层模型，将每种癌症的相对风险分解为共享的空间与时间成分。
• 每个共享成分与一组癌症相关联，捕捉这些疾病之间的共同空间与时间趋势。
• 空间与时间随机效应分别使用条件自回归（CAR）和随机游走先验进行建模，以考虑空间相关性与时间依赖性。
• 模型估计每个相关癌症的共享成分相对权重（缩放参数），从而可解释成分的重要性。
• 为共享成分及其方差分配先验分布，并通过马尔可夫链蒙特卡洛（MCMC）抽样实现完整贝叶斯推断。
• 通过偏差信息准则（DIC）进行模型比较，并通过后验预测检查与残差分析评估模型拟合度。

实验结果

研究问题

• RQ1 2005至2009年间，伊朗七种常见癌症类型的发病率在空间与时间上的模式如何变化？
• RQ2 哪些共享潜在成分解释了多种癌症之间的共同地理与时间趋势？
• RQ3 每个共享成分对个体癌症在时间和空间上的发病率风险的相对贡献是什么？
• RQ4 与单变量模型相比，联合建模方法在估计精度与热点区域检测方面有何改进？
• RQ5 每种癌症的高风险与低风险地理聚集区是什么？不同疾病对之间的聚集模式如何比较？

主要发现

• Sistan and Baluchestan 省在所有时间周期内对七种癌症的相对风险均最低。
• 高风险省份包括 Razavi Khorasan、Semnan、Gilan、Mazandaran、Yazd、Isfahan、East Azerbaijan、Fars、West Azerbaijan、Kurdistan、Tehran、Ardebil 和 Golestan。
• 食道癌与胃癌表现出高度相似的空间模式，膀胱癌与结直肠癌亦然，乳腺癌与前列腺癌亦呈现相似模式。
• 肺癌表现出与其他所有癌症明显不同的空间模式，表明其具有独特的风险因素。
• 共享成分的时间效应在五年期间相对稳定，表明潜在风险趋势在短期内变化有限。
• 通过共享成分在相关疾病间借用信息，该模型显著提高了对罕见癌症的估计精度。