A mean score method for missing and auxiliary covariate data in regression models
We consider regression analysis when incomplete or auxiliary covariate data are available for all study subjects and, in addition, for a subset called the validation sample, true covariate data of interest have been ascertained. The term auxiliary data refers to data not in the regression model, but...
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Oxford University Press
1995
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fthighwire:oai:open-archive.highwire.org:biomet:82/2/299 2023-05-15T16:28:55+02:00 A mean score method for missing and auxiliary covariate data in regression models REILLY, MARIE PEPE, MARGARET SULLIVAN 1995-06-01 00:00:00.0 text/html http://biomet.oxfordjournals.org/cgi/content/short/82/2/299 https://doi.org/10.1093/biomet/82.2.299 en eng Oxford University Press http://biomet.oxfordjournals.org/cgi/content/short/82/2/299 http://dx.doi.org/10.1093/biomet/82.2.299 Copyright (C) 1995, Biometrika Trust Articles TEXT 1995 fthighwire https://doi.org/10.1093/biomet/82.2.299 2007-06-25T02:57:16Z We consider regression analysis when incomplete or auxiliary covariate data are available for all study subjects and, in addition, for a subset called the validation sample, true covariate data of interest have been ascertained. The term auxiliary data refers to data not in the regression model, but thought to be informative about the true missing covariate data of interest. We discuss a method which is nonparametric with respect to the association between available and missing data, allows missingness to depend on available response and covariate values, and is applicable to both cohort and case-control study designs. The method previously proposed by Flanders & Greenland (1991) and by Zhao & Lipsitz (1992) is generalised and asymptotic theory is derived. Our expression for the asymptotic variance of the estimator provides intuition regarding performance of the method. Optimal sampling strategies for the validation set are also suggested by the asymptotic results. Text Greenland HighWire Press (Stanford University) Greenland Biometrika 82 2 299 314 |
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HighWire Press (Stanford University) |
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English |
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Articles |
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Articles REILLY, MARIE PEPE, MARGARET SULLIVAN A mean score method for missing and auxiliary covariate data in regression models |
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Articles |
description |
We consider regression analysis when incomplete or auxiliary covariate data are available for all study subjects and, in addition, for a subset called the validation sample, true covariate data of interest have been ascertained. The term auxiliary data refers to data not in the regression model, but thought to be informative about the true missing covariate data of interest. We discuss a method which is nonparametric with respect to the association between available and missing data, allows missingness to depend on available response and covariate values, and is applicable to both cohort and case-control study designs. The method previously proposed by Flanders & Greenland (1991) and by Zhao & Lipsitz (1992) is generalised and asymptotic theory is derived. Our expression for the asymptotic variance of the estimator provides intuition regarding performance of the method. Optimal sampling strategies for the validation set are also suggested by the asymptotic results. |
format |
Text |
author |
REILLY, MARIE PEPE, MARGARET SULLIVAN |
author_facet |
REILLY, MARIE PEPE, MARGARET SULLIVAN |
author_sort |
REILLY, MARIE |
title |
A mean score method for missing and auxiliary covariate data in regression models |
title_short |
A mean score method for missing and auxiliary covariate data in regression models |
title_full |
A mean score method for missing and auxiliary covariate data in regression models |
title_fullStr |
A mean score method for missing and auxiliary covariate data in regression models |
title_full_unstemmed |
A mean score method for missing and auxiliary covariate data in regression models |
title_sort |
mean score method for missing and auxiliary covariate data in regression models |
publisher |
Oxford University Press |
publishDate |
1995 |
url |
http://biomet.oxfordjournals.org/cgi/content/short/82/2/299 https://doi.org/10.1093/biomet/82.2.299 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_relation |
http://biomet.oxfordjournals.org/cgi/content/short/82/2/299 http://dx.doi.org/10.1093/biomet/82.2.299 |
op_rights |
Copyright (C) 1995, Biometrika Trust |
op_doi |
https://doi.org/10.1093/biomet/82.2.299 |
container_title |
Biometrika |
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82 |
container_issue |
2 |
container_start_page |
299 |
op_container_end_page |
314 |
_version_ |
1766018602789502976 |