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，人们经常关注检测最近趋势的变化。在回顾性和前瞻性队列研究中寻找疾病模式时，识别队列中此类变化的能力是一个重要问题。拟议的研究旨在开发一种方法，以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，我们将考虑简单高斯连接点回归模型扩展到具有K个响应和可能的非齐次离差参数的logistic回归。我们将推导并实现与软件的方法，估计和测试的模型参数的基础上的条件最大似然。由于模型中连接点（变点）的位置是未知的，该方法将采用迭代条件最大化算法来寻求似然方程的解。为了检验最终模型的有效性以及评估最终一组检测到的变化点的显著性，我们将依次应用参数自助法。在我们的设置中，所有由此产生的估计和测试程序的一致性和一般适当性的条件也将被推导出来。此外，我们将通过模拟研究比较连接点逻辑回归模型与惩罚样条（P-样条）和多元自适应回归样条（MARS）模型的性能。最后，我们还将开发的模型应用到癌症死亡率的纵向数据集之间的路易斯维尔VC队列的成员，现在退休的化学工人，直到1996年。该数据集可通过路易斯维尔大学健康监测计划获得。使用联合点逻辑回归模型，我们将确定队列癌症发生率与国家参考人群相比的纵向变化（时间变点）模式，调整不同生产地区疾病的时间聚集性。路易斯维尔VC队列数据的使用将使我们能够说明我们的方法，在一个创新的应用程序，以监测职业病，并比较其有效性与标准的方法，鉴于文献中提供的这个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.