Data/hora: 09/06/2024 13:21:58

Provedor de dados: 	31	
País: 	United States	
Título: 	Multiple imputation of missing values	
Autores: 	Royston,   Patrick.	
Data: 	2011-10-05	
Ano: 	2004	
Palavras-chave: 	Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.	
Resumo: 	Following the seminal publications of Rubin about thirty years ago, statisticians have become increasingly aware of the inadequacy of “complete-case” analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates, and a complete-case analysis may omit as many as half of the available cases. Hotdeck imputation was implemented in Stata in 1999 by Mander and Clayton. However, this technique may perform poorly when many rows of data have at least one missing value. This article describes an implementation for Stata of the MICE method of multiple multivariate imputation described by van Buuren, Boshuizen, and Knook (1999). MICE stands for multivariate imputation by chained equations. The basic idea of data analysis with multiple imputation is to create a small number (e.g., 5–10) of copies of the data, each of which has the missing values suitably imputed, and analyze each complete dataset independently. Estimates of parameters of interest are averaged across the copies to give a single estimate. Standard errors are computed according to the “Rubin rules”, devised to allow for the between- and within-imputation components of variation in the parameter estimates. This article describes five ado-files. mvis creates multiple multivariate imputations. uvis imputes missing values for a single variable as a function of several covariates, each with complete data. micombine fits a wide variety of regression models to a multiply imputed dataset, combining the estimates using Rubin’s rules, and supports survival analysis models (stcox and streg), categorical data models, generalized linear models, and more. Finally, misplit and mijoin are utilities to interconvert datasets created by mvis and by the miset program from John Carlin and colleagues. The use of the routines is illustrated with an example of prognostic modeling in breast cancer.	
Tipo: 	Journal Article	
Idioma: 	Inglês	
Identificador: 	st0067

http://purl.umn.edu/116244	
Relação: 	Stata Journal&gt;Volume 4, Number 3, 3rd Quarter 2004	
Formato: 	15