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.
翻译:在诊断和病人管理方面,病理学测试是现代保健的核心。综合病理学结果提供了研究健康和医学方面基本和应用问题的机会,但数据分析挑战却出现,因为医学从业者之间的测试剖面不同,导致数据缺失。在这项研究中,我们对丙型肝炎(HCV)感染的实验室诊断进行了分析性调查,重点是如何最大限度地扩大常规病理学数据的预测值。我们建议使用InflV-溢流量措施,帮助在使用多重估算时建立估算模型。14 320名15-100岁社区病人的数据通过ACT病理学(堪培拉医院,澳大利亚)获得,但数据分析分析显示分析结果显示,流动和流量分析结果显示,流动和流量分析结果显示,流动数据预测结果显示,通过实验室预测结果显示,预测结果显示,通过实验室预测结果显示,通过实验室预测结果显示,预测结果显示,通过实验室预测结果显示,通过实验室预测结果显示,通过实验室预测结果显示,通过实验室预测结果显示,通过实验室预测结果显示,通过实验室预测结果显示,通过实验室预测结果显示,通过实验室预测结果显示,结果显示,通过实验室预测结果显示,通过实验室推变异变变变。