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

拟议的研究旨在探讨数据与基于索赔的数据(主要是医疗补助和私人保险数据)的联系如何改善肯塔基州癌症登记中心(KCR)数据中的共病和治疗变量，并通过概率联系评估数据联系的准确性和偏差，以及比较基于原始KCR数据和改进的KCR数据的统计分析中的统计估计的差异。 目标： 1)检查概率数据链接过程以及截止值将如何在识别真实匹配时引入偏差。 2)检查与医疗补助数据相关联如何改进诸如合并症和治疗信息等变量的登记数据。 3)审查与私人保险索赔数据，如Humana、Anhim和州雇员保险数据的联系，如何改进诸如合并症和治疗信息等变量的登记数据。 4)研究Medicare、Medicaid和私人保险索赔数据的组合如何改进共病、治疗信息和某些护理质量措施的登记数据。 5)比较原始注册数据和扩展注册数据(如Logistic回归模型和Cox回归生存模型)在建模统计分析中的统计估计。

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.