美国县级乳腺癌发病率的基于数据评估：可改变与不可改变因素的影响

Modeling county level breast cancer survival data using a covariate-adjusted frailty proportional hazards model

Understanding the factors that explain differences in survival times is an important issue for establishing policies to improve national health systems. Motivated by breast cancer data arising from the Surveillance Epidemiology and End Results program, we propose a covariate-adjusted proportional hazards frailty model for the analysis of clustered right-censored data. Rather than incorporating exchangeable frailties in the linear predictor of commonly-used survival models, we allow the frailty distribution to flexibly change with both continuous and categorical cluster-level covariates and mod…

Nonlinear State Space Modeling and Control of the Impact of Patients' Modifiable Lifestyle Behaviors on the Emergence of Multiple Chronic Conditions

The emergence and progression of multiple chronic conditions (MCC) over time often form a dynamic network that depends on patient's modifiable risk factors and their interaction with non-modifiable risk factors and existing conditions. Continuous time Bayesian networks (CTBNs) are effective methods for modeling the complex network of MCC relationships over time. However, CTBNs are not able to effectively formulate the dynamic impact of patient's modifiable risk factors on the emergence and progression of MCC. Considering a functional CTBN (FCTBN) to represent the underlying structure of the MC…

用于综合女性乳腺癌患病率的登记与调查数据的贝叶斯分层模型

Bayesian Hierarchical Model for Synthesizing Registry and Survey Data on Female Breast Cancer Prevalence

本文提出两阶段贝叶斯模型综合癌症登记与调查数据，以估计美国密苏里州县级女性乳腺癌患病率，发现联合使用多源数据并考虑数据源系统差异能显著提升估计准确性。

A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions

Multiple chronic conditions (MCC) are one of the biggest challenges of modern times. The evolution of MCC follows a complex stochastic process that is influenced by a variety of risk factors, ranging from pre-existing conditions to modifiable lifestyle behavioral factors (e.g. diet, exercise habits, tobacco use, alcohol use, etc.) to non-modifiable socio-demographic factors (e.g., age, gender, education, marital status, etc.). People with MCC are at an increased risk of new chronic conditions and mortality. This paper proposes a model predictive control functional continuous time Bayesian netw…

使用SBART分析具有未观测协变量的空间聚类生存数据

Analysis of spatially clustered survival data with unobserved covariates using SBART

针对大型空间聚类生存数据，提出基于软贝叶斯加法回归树（SBART）的非参数方法，处理未知函数形式及未观测协变量，并在佛罗里达州乳腺癌数据中验证其优于现有方法。