Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study
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چکیده
منابع مشابه
Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study
BACKGROUND The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model. METHODS Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500...
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Missing covariate values is a common problem in a survival data research. The aim of this study is to compare the use of the multiple imputation (MI) and last observation carried forward (LOCF) methods for handling missing covariate values in the Cox proportional hazards (PH) regression model. The comparisons between the methods are based on simulated data. The missingness mechanism is assumed ...
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MOTIVATION There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Cox's proportional hazards model. To avoid th...
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BACKGROUND There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. METHODS Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Mu...
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ژورنال
عنوان ژورنال: BMC Medical Research Methodology
سال: 2010
ISSN: 1471-2288
DOI: 10.1186/1471-2288-10-112