Modeling Breast Cancer Recurrence Using New Statistical Methods for Semi-Markov P

描述（由申请人提供）：纵向研究使我们能够调查慢性疾病的自然史。对于某些疾病，其演变过程的特点是一系列健康状态之间的过渡。例如，在癌症中，受试者可以从无癌症状态过渡到早期疾病，并随后过渡到晚期疾病。半马尔可夫过程为建模多状态疾病过程提供了一个灵活的框架。然而，将半马尔可夫过程应用于多状态疾病研究的估计方法有限。现有的估计方法需要对这一过程强加许多假设，这些假设在生物学上可能对许多疾病不合理。在小组观察下收集的数据尤其缺乏估计方法。在这个建议中，我们开发灵活的和生物学上适当的方法来估计纵向多状态疾病研究中的半马尔可夫过程。我们提出了一类时间变换函数，它允许我们桥接半马尔可夫过程和马尔可夫过程，以便利用可用于马尔可夫过程的直接估计方法。然后，我们建议使用混合模型，加上一组生物学上合理的假设，来扩展这些方法，以允许在面板观察下进行估计。这些方法将用于估计乳腺癌的复发率和乳腺癌发生后的第二原发性乳腺癌的发病率。利用乳腺癌监测联盟13年的纵向数据，我们将证明我们的新方法在估计与马尔可夫过程模型和比例风险生存模型相关的乳腺癌事件状态转移率方面的改进性能。

DESCRIPTION (provided by applicant): Longitudinal studies allow us to investigate the natural history of chronic diseases. For some diseases the evolution of the process is characterized by transitions between a series of health states. For example, in cancer, subjects may transition from a cancer-free state to early stage disease, and subsequently to late stage disease. Semi-Markov processes provide a flexible framework for modeling multi-state disease processes. However, limited estimation methods are available for the application of Semi-Markov processes to the study of multi-state disease. Existing estimation methods require the imposition of numerous assumptions on the process, which may not be biologically plausible for many diseases. The lack of estimation methods is particularly acute for data collected under panel observation. In this proposal we develop flexible and biologically appropriate methods for estimating Semi-Markov processes in longitudinal multi-state disease studies. We propose a class of time transformation functions that allows us to bridge Semi-Markov and Markov processes in order to harness the straightforward estimation methods available for Markov processes. We then propose to use mixture models, coupled with a set of biologically reasonable assumptions, to extend these methods to allow for estimation under panel observation. These methods will be applied to the estimation of breast cancer recurrence rates and rates of second primary breast cancers subsequent to an incident breast cancer. Using 13 years of longitudinal data from the Breast Cancer Surveillance Consortium, we will demonstrate improved performance of our novel methods for estimating transition rates among breast event states relative to Markov process models and proportional hazards survival models. PUBLIC HEALTH RELEVANCE: Semi-Markov processes provide a flexible framework for modeling multi-state diseases characterized by transitions between a series of health states. However, limited estimation methods are available for the application of such processes. We will develop flexible methods for estimating Semi-Markov processes and will use our novel estimation methods to study rates of breast cancer recurrence and second primary breast cancers subsequent to an incident breast cancer using longitudinal data from the Breast Cancer Surveillance Consortium.