Consulta: subjectFacets:"Micombine"
Registros recuperados: 4
Data/hora: 16/06/2024 07:07:00

Multiple imputation of missing values: further update of ice, with an emphasis on interval censoring
Provedor de dados: 31
Autores: Royston,   Patrick.
Multiple imputation of missing data continues to be a topic of considerable interest and importance to applied researchers. In this article, the ice package for multiple imputation is further updated. Special attention in this article is paid to imputing interval-censored observations, and a suggestion to use imputation of right-censored survival data to elucidate covariate effects graphically.
Tipo: Article
Palavras-chave: Ice; Uvis; Micombine; Ice_reformat; Multiple imputation; Interval censoring; Visualization; Censored survival data; Research Methods/ Statistical Methods.
Ano: 2007
URL: http://purl.umn.edu/119290

Multiple imputation of missing values
Provedor de dados: 31
Autores: Royston,   Patrick.
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...
Tipo: Journal Article
Palavras-chave: Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.
Ano: 2004
URL: http://purl.umn.edu/116244

A new framework for managing and analyzing multiply imputed data in Stata
Provedor de dados: 31
Autores: Carlin,   John B.; Galati,   John C.; Royston,   Patrick.
A new set of tools is described for performing analyses of an ensemble of datasets that includes multiple copies of the original data with imputations of missing values, as required for the method of multiple imputation. The tools replace those originally developed by the authors. They are based on a simple data management paradigm in which the imputed datasets are all stored along with the original data in a single dataset with a vertically stacked format, as proposed by Royston in his ice and micombine commands. Stacking into a single dataset simplifies the management of the imputed datasets compared with storing them individually. Analysis and manipulation of the stacked datasets is performed with a new prefix command, mim, which can accommodate data...
Tipo: Article
Palavras-chave: Mim; Mimstack; Ice; Micombine; Miset; Mifit; Multiple imputation; Missing data; Missing at random; Research Methods/ Statistical Methods.
Ano: 2008
URL: http://purl.umn.edu/120928

Multiple imputation of missing values: update
Provedor de dados: 31
Autores: Royston,   Patrick.
This article describes a substantial update to mvis, which brings it more closely in line with the feature set of S. van Buuren and C. G. M. Oudshoorn’s implementation of the MICE system in R and S-PLUS (for details, see http://www.multiple-imputation.com). To make a clear distinction from mvis, the principal program of the new Stata release is called ice. I will give details of how to use the new features and a practical illustrative example using real data. All the facilities of mvis are retained by ice. Some improvements to micombine for computing estimates from multiply imputed datasets are also described.
Tipo: Journal Article
Palavras-chave: Ice; Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.
Ano: 2005
URL: http://purl.umn.edu/117511