标题： Routine Pathology Variables 的多重插补对丙型肝炎感染实验室诊断的影响 显示英文标题

标题： The Effect of Multiple Imputation of Routine Pathology Variables on Laboratory Diagnosis of Hepatitis C Infection

摘要： 病理学检测在现代医疗中对于诊断和患者管理至关重要。 汇总的病理结果为健康和医学中的基础和应用问题研究提供了机会，但由于不同医务人员的检测项目有所不同，导致数据缺失，出现了数据分析挑战。 在本研究中，我们对丙型肝炎（HCV）感染的实验室诊断进行了分析调查，并专注于如何最大化常规病理数据的预测价值。 我们建议在使用多重插补时使用流入-流出度量来帮助构建插补模型。 通过ACT病理学（澳大利亚堪培拉医院）访问了14,320名年龄在15至100岁之间的社区患者的数据。 计算了流入和流出，以确定哪些变量可能是缺失值的强大预测因素。 使用可用案例分析和多重插补来处理数据集中的缺失值。 逻辑回归模型和逐步选择方法用于分析插补后的数据集。 比较了所有方法的预测能力。 在多重插补数据上的模型预测能力与基于完整数据的模型预测能力相似。 多重插补的优势在于它允许将所有完成的变量包含在逻辑模型中，从而识别出更广泛的一组检测结果，这些结果可能导致增强的HCV实验室预测。 多重插补是一种重要的统计资源，使研究中的每个个体都能将其提供的任何数据贡献给分析。 MI结合流入和流出的值可以识别HepC感染的潜在预测因素。 变量年龄、性别和丙氨酸氨基转移酶已被证明是HCV感染的强效实验室预测因素。 显示英文摘要

摘要： Pathology tests are central to modern healthcare in terms of diagnosis and patient management. Aggregated pathology results provide opportunities for research into fundamental and applied questions in health and medicine, but data analytic challenges appear since test profiles vary between medical practitioners, resulting in missing data. In this study we provide an analytical investigation of the laboratory diagnosis of Hepatitis C (HCV) infection and focus on how to maximize the predictive value of routine pathology data. We recommend using the Influx - Outflux measures to help construct the imputation model when using multiple imputation. Data from 14,320 community-patients aged 15 - 100 years were accessed via ACT Pathology (The Canberra Hospital, Australia). Influx and Outflux were calculated to identify which variables were potentially powerful predictors of missing values. Available Case analysis and Multiple Imputation were used to accommodate missing values in the dataset. Logistic regression model and stepwise selection method were used for analysing the imputed datasets. The predictive power of all methods was compared. The predictive power of the models on multiply imputed data was similar to the power of the models based on complete data. The advantage of multiply imputed data was that it allowed for the inclusion of all the completed variables in the logistic models, thus identifying a broader selection of test results that could lead to the enhanced laboratory prediction of HCV. Multiple imputation is an important statistical resource allowing all individuals in a study to contribute whatever data they have supplied to the analysis. MI in combination with the values of Influx and Outflux identifies potential predictors of HepC infection. Variables age, gender and alanine aminotransferase have been shown to be strong laboratory predictors of HCV infection.