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Comparison of methods for handling missing data on immunohistochemical markers in survival analysis of breast cancer

机译:处理免疫组织化学标志物中缺失数据的比较乳腺癌存活分析

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Background:Tissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer.
机译:背景:组织微阵列(TMA)越来越多地用于产生临床流行病学研究中肿瘤的分子表型的数据,例如疾病预后的研究。但是,TMA数据特别容易发生缺失。可以使用各种处理缺失数据的方法。然而,各种方法的有效性取决于缺失数据的结构,并且少数经验研究处理分子病理学中缺失的数据。本研究的目的是调查四种常用方法,处理缺失的乳腺癌分子病理决定簇的缺失数据的处理缺失的数据。

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