IGF::OT::IGF EVALUATING FEASIBILITY OF AND POTENTIAL BIASES IN SUPPLEMENTING CANCER REGISTRY DATA USING EXTERNAL SOURCES

本研究旨在探讨基于索赔的数据（主要是医疗补助和私人保险数据）的数据链接如何改善肯塔基州癌症登记处（KCR）数据中的合并症和治疗变量，并通过概率链接评估数据链接的准确性和偏倚，并比较基于原始KCR数据和增强KCR数据的统计分析中统计估计值之间的差异。 目的： 第一章 检查概率数据链接过程以及截止值如何在识别真正匹配时引入偏差。 （二） 检查与医疗补助数据的链接如何改善合并症和治疗信息等变量的注册数据。 第三章 研究如何与私人保险索赔数据（如Humana，Anthem和州雇员保险数据）链接，以改善合并症和治疗信息等变量的注册数据。 四、 检查医疗保险，医疗补助和私人保险索赔数据的组合如何改善合并症，治疗信息和某些护理质量措施的注册数据。 第五章） 比较原始登记研究数据和扩增登记研究数据之间建模统计分析中的统计估计值，例如逻辑回归模型和考克斯回归生存模型。

The proposed study is to examine how data linkage with claims based data, primarily Medicaid and private insurance data, will improve the comorbidity and treatment variables in the Kentucky Cancer Registry (KCR) data, and evaluate the accuracy and biases of data linkage through probabilistic linkage, and compare differences between statistical estimate in statistical analyses based on the original KCR data and the enhanced KCR data. Objectives: 1) Examine the probabilistic data linkage process and how cutoff values will introduce biases in identifying true matches. 2) Examine how linking with Medicaid data improves the registry data for variables such as comorbidity and treatment information. 3) Examine how linking with private insurance claims data, such as Humana, Anthem and state employee insurance data, improves the registry data for variables such as comorbidity and treatment information. 4) Examine how combinations of Medicare, Medicaid, and private insurance claims data improves the registry data for comorbidity, treatment information and certain quality of care measures. 5) Compare statistics estimates in modeling statistical analyses between the original registry data and augmented registry data, such as logistic regression models and Cox regression survival models.