Statistical Methods for Outcome-Dependent Sampling

描述(申请人提供)：本赠款申请的主要目标是开发和评估改进的统计方法，以设计和分析在随机效应模型和非线性协变量效应下使用结果依赖抽样(Ods)计划进行的流行病学研究。将开发简单的消耗臭氧层物质设计的扩展，通过允许抽样概率取决于结果和辅助协变量来进一步改进研究。将研究在特定环境下导致更具成本效益的设计的抽样策略。拟议的方法对癌症和环境研究特别有用，因为辅助信息和昂贵的暴露评估是经常面临的挑战。该提案由四个项目组成：第一个项目涉及消耗臭氧层物质设计的线性混合模型回归分析，其结果是连续的。对随机效应模型中的回归参数进行推断的两种新方法将被研究：半参数经验似然方法用于观察到的消耗臭氧层物质样本，以及当结果变量的值对于潜在队列已知时用于进一步提高效率的伪似然方法。第二个项目涉及有效的正式文件系统设计，其中抽样概率取决于连续结果和辅助协变量。再次，针对不同的数据结构，提出了两种新的处理方法。第三个项目涉及依赖于结果变量和辅助协变量的消耗臭氧层物质的广义线性混合效应模型分析。最后一个项目研究了非参数非参数建模的新统计方法，该方法用于非参数非参数模型中的非参数变量的影响。提出了两种方法：非参数局部经验似然方法和局部经验似然方法，前者用于通过ODS设计收集数据时关于非线性协变量效应的推断，后者适用于更一般的情形，其中该方法依赖于结果和离散的辅助协变量。还考虑了处理连续辅助协变量的另一种方法。将通过理论调查和模拟研究来严格检查每种建议方法的优点和缺点。将与现有方法进行比较。将开发相关软件。将分析正在进行的环境暴露影响的流行病学研究以及对癌症和其他疾病的影响的数据集。

DESCRIPTION (provided by applicant): The main objective of this grant application is to develop and evaluate improved statistical methods for the design and analysis of epidemiologic studies conducted with outcome-dependent sampling (ODS) schemes under random effects models and nonlinear covariates effects. Extension of the simple ODS design to further improve the study by allowing the sampling probability to depend on the outcome and auxiliary covariates will be developed. Sampling strategies that lead to a more cost-effective design in a given setting will be investigated. The proposed methods are particularly useful for cancer and environmental research because auxiliary information and expensive exposure assessment are frequent challenges. The proposal consists of four projects: The first project deals with linear mixed model regression analysis for an ODS design with a continuous outcome. Two new methods for making inferences about regression parameters in random effects models will be studied: a semi-parametric empirical likelihood approach for observed ODS sample, and a pseudo-likelihood approach for further improving efficiency when the values of the outcome variable are known for the underlying cohort. The second project concerns an efficient ODS design where the sampling probability depends on a continuous outcome and auxiliary covariates. Again, two new methods dealing with different available data structure are proposed. The third project deals with generalized linear mixed effects model analysis for an ODS that depends on both outcome and auxiliary covariates. The last project investigates new statistical methods for nonparametric modeling of nonlinear covariates effects in the logistic regression model under ODS designs. Two methods are proposed: a nonparametric local empirical likelihood method for inference about nonlinear covariates effects when data is collected via ODS designs and a local empirical likelihood method for more general settings where the ODS design depends on outcome and discrete auxiliary covariates. Another method for dealing with continuous auxiliary covariates is also considered. The strengths and weaknesses of each proposed method will be critically examined via theoretical investigations and simulation studies. Comparisons with existing methods will be conducted. Related software will be developed. Data sets from ongoing epidemiologic studies of the effects of environmental exposures, and on cancer and other diseases will be analyzed.