Logistic joinpoint regression model in cohort studies

描述（由申请人提供）：在研究趋势数据（如癌症死亡率和发病率数据）时，人们经常关注于发现近期趋势的变化。在寻找疾病模式时，识别队列中此类变化的能力对于回顾性和前瞻性队列研究都是一个重要问题。提出的研究旨在开发一种方法来扩大连接点回归模型在检测疾病趋势变化方面的适用性。具体来说，我们将考虑将简单高斯连接点回归模型扩展到具有K响应和可能非均匀分散参数的逻辑回归。我们将在条件极大似然的基础上推导并用软件实现模型参数的估计和检验方法。由于模型中连接点（变化点）的位置未知，该方法采用迭代条件最大化算法求解似然方程。为了检验最终模型的有效性以及评估最终检测到的变化点集合的重要性，我们将依次应用参数自举方法。还应推导出在我们的设置中所有结果估计和测试程序的一致性和一般适当性的条件。此外，我们将通过仿真研究比较连接点逻辑回归模型与惩罚样条（p样条）和多变量自适应样条（MARS）模型的性能。最后，我们还将开发的模型应用于路易斯维尔VC队列中现已退休的化学工人的癌症死亡率的纵向数据集，直到1996年。该数据集可通过路易斯维尔大学健康监测项目获得。使用联结点逻辑回归模型，我们将确定与国家参考人群相比，队列癌症发病率的纵向变化模式（时间变化点），调整疾病在不同生产区域的时间聚类。路易斯维尔VC队列数据的使用将使我们能够说明我们在监测职业病的创新应用中的方法，并将其与标准方法的有效性进行比较，考虑到文献中可用的该数据集的不同分析的多样性。

DESCRIPTION (provided by applicant): In studying trend data such as cancer mortality and incidence data one is frequently concerned with detecting a change in recent trend. The ability to identify such changes in a cohort is an important problem for both retrospective and prospective cohort studies when looking for disease patterns. The proposed research seeks to develop a method to broaden the applicability of the join point regression model in detecting changes in disease trends. Specifically, we shall consider the extension of the simple Gaussian joinpoint regression model to logistic regression with K responses and possibly non-homogenous dispersion parameters. We shall derive and implement with the software the method for estimation and testing of the model parameters on the basis of the conditional maximum likelihood. Since the location of the joinpoints (change points) in the model is unknown the method would employ the iterative conditional maximization algorithm in seeking the solutions of the likelihood equations. In order to test the validity of the final model as well as to assess the significance of the final set of detected change points we shall sequentially apply the parametric bootstrap method. The conditions for consistency and general appropriateness of all the resulting estimation and testing procedures in our setting shall be also derived. Additionally, we shall compare via simulation studies the performance of the joinpoint logistic regression model versus that of penalized splines (P-splines) and multivariate adaptive regression splines (MARS) models. Finally we shall also apply the developed model to the longitudinal dataset on cancer mortality among the members of the Louisville VC cohort of now retired chemical workers up until 1996. The dataset is available via the University of Louisville Health Surveillance Program. Using the joinpoint logistic regression model we shall determine the pattern of longitudinal changes (time change-points) in the cohort cancer occurrences as compared with the state reference population, adjusting for the temporal clustering of the disease in the different production areas. The use of the Louisville VC cohort data shall allow us to illustrate our approach in an innovative application to monitoring occupational diseases and to compare its effectiveness with that of the standard methodology in view of the multiplicity of different analysis of this dataset available in the literature.