Logistic joinpoint regression model in cohort studies

描述(由申请人提供)：在研究癌症死亡率和发病率等趋势数据时，人们经常关注检测最近趋势的变化。在寻找疾病模式时，识别队列中这种变化的能力对于回顾和前瞻性队列研究都是一个重要的问题。这项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。具体地说，我们将考虑将简单的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散参数的Logistic回归。我们将推导并用软件实现基于条件最大似然的模型参数估计和检验方法。由于模型中连接点(变点)的位置未知，该方法将采用迭代条件最大化算法来求解似然方程。为了测试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，我们将顺序应用参数自举方法。还应推导出在我们的设置中所有由此产生的评估和测试程序的一致性和一般适当性的条件。此外，我们将通过模拟研究将连接点Logistic回归模型的性能与惩罚样条(P-Splines)和多变量自适应回归Splines(MARS)模型的性能进行比较。最后，我们还将把开发的模型应用于路易斯维尔风险投资队列成员的癌症死亡率的纵向数据集，这些成员现在已经退休，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。利用连接点Logistic回归模型，根据疾病在不同产区的时间聚集性，确定队列癌症发病率与国家参考人群相比的纵向变化模式(时间变化点)。路易斯维尔风险投资队列数据的使用将使我们能够说明我们在职业病监测的创新应用中的方法，并鉴于文献中对该数据集的多种不同分析，将其有效性与标准方法进行比较。

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.