Imputing missing covariate values for the Cox model
نویسندگان
چکیده
منابع مشابه
Imputing missing covariate values for the Cox model
Multiple imputation is commonly used to impute missing data, and is typically more efficient than complete cases analysis in regression analysis when covariates have missing values. Imputation may be performed using a regression model for the incomplete covariates on other covariates and, importantly, on the outcome. With a survival outcome, it is a common practice to use the event indicator D ...
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Missing value is an unavoidable problem when dealing with real world data sources, and various approaches for dealing with missing data have been developed. In fact, it is very important to consider the imputation ordering (ordering means which missing value should be imputed at first with the help of a specific criterion) during the imputation process, because not all attributes have the same ...
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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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ژورنال
عنوان ژورنال: Statistics in Medicine
سال: 2009
ISSN: 0277-6715
DOI: 10.1002/sim.3618