Department of Medical Epidemiology and Biostatistics, 27106Karolinska Institutet, Stockholm, Sweden.
Stat Methods Med Res. 2022 Dec;31(12):2415-2430. doi: 10.1177/09622802221122410. Epub 2022 Sep 18.
The few existing statistical models of breast cancer recurrence and progression to distant metastasis are predominantly based on multi-state modelling. While useful for summarising the risk of recurrence, these provide limited insight into the underlying biological mechanisms and have limited use for understanding the implications of population-level interventions. We develop an alternative, novel, and parsimonious approach for modelling latent tumour growth and spread to local and distant metastasis, based on a natural history model with biologically inspired components. We include marginal sub-models for local and distant breast cancer metastasis, jointly modelled using a copula function. Different formulations (and correlation shapes) are allowed, thus we can incorporate and directly model the correlation between local and distant metastasis flexibly and efficiently. Submodels for the latent cancer growth, the detection process, and screening sensitivity, together with random effects to account for between-patients heterogeneity, are included. Although relying on several parametric assumptions, the joint copula model can be useful for understanding - potentially latent - disease dynamics, obtaining patient-specific, model-based predictions, and studying interventions at a population level, for example, using microsimulation. We illustrate this approach using data from a Swedish population-based case-control study of postmenopausal breast cancer, including examples of useful model-based predictions.
现有的少数几种乳腺癌复发和远处转移的统计模型主要基于多状态模型。虽然这些模型对于总结复发风险很有用，但它们对潜在的生物学机制提供的了解有限，对于理解人群干预的影响也有限。我们开发了一种替代的、新颖的、简约的方法，用于对潜在的肿瘤生长和局部及远处转移进行建模，该方法基于具有生物学启发成分的自然史模型。我们包括局部和远处乳腺癌转移的边缘子模型，使用 Copula 函数联合建模。允许不同的公式（和相关形状），因此我们可以灵活有效地纳入和直接建模局部和远处转移之间的相关性。包括潜在癌症生长、检测过程和筛查敏感性的子模型，以及用于解释患者间异质性的随机效应。尽管依赖于几个参数假设，但联合 Copula 模型可用于理解潜在疾病动态，获得基于模型的患者特定预测，并在人群水平上进行干预研究，例如使用微观模拟。我们使用来自瑞典基于人群的绝经后乳腺癌病例对照研究的数据来说明这种方法，并提供了一些有用的基于模型预测的示例。
