Multiple imputations and the missing censoring indicator model
نویسندگان
چکیده
منابع مشابه
Multiple imputations and the missing censoring indicator model
Semiparametric random censorship (SRC) models (Dikta, 1998) provide an attractive framework for estimating survival functions when censoring indicators are fully or partially available. When there are missing censoring indicators (MCIs), the SRC approach employs a model-based estimate of the conditional expectation of the censoring indicator given the observed time, where the model parameters a...
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In cancer survival studies, death certificate information can be missing, or incidental and fatal occurrences may be indistinguishable for some subjects, leading to missing censoring indicators (MCIs). For the framework of right censored data with MCIs, sub-density function kernel estimators play a significant role for estimating a survival function. Data-driven bandwidths for computing these k...
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In situations with missing data, statistical analyses are usually limited to subjects with complete data. However, such estimates may be biased. The method of 'filling in' missing data is called imputation. This article aimed to present a multiple imputation method. From a data set of 470 surgical patients, logistic models were developed for death as the outcome. Two incomplete data sets were g...
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Re: " Linking missing data to study outcomes using multiple imputations " Dear Editor: In our analysis of data from the Canadian Community Health Survey examining body mass index (BMI) among immigrant and non-immigrant Canadian youth, multiple imputation (MI) was used to address missing data. 1 We believe that our approach to MI did not bias the study's main findings, which showed a statistical...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2011
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2010.08.005