Multiple Imputation of Covariates by Substantive-model Compatible Fully Conditional Specification
نویسندگان
چکیده
منابع مشابه
Multiple imputation of covariates by substantive model compatible fully conditional specification
Multiple imputation (MI) is a practical, principled approach to handling missing data. When used to impute missing values in covariates of regression models, imputation models may be mis-specified if they are not compatible with the substantive model of interest for the outcome. In this article we introduce the smcfcs command, which imputes covariates by substantive model compatible fully condi...
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Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of multiple imputation may imp...
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The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the specified conditional densities can be incompatible, and therefore the stationary distribution to w...
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ژورنال
عنوان ژورنال: The Stata Journal: Promoting communications on statistics and Stata
سال: 2015
ISSN: 1536-867X,1536-8734
DOI: 10.1177/1536867x1501500206