The effects of imputing the missing standard deviations on the standard error of meta analysis estimates

A common problem in the meta analysis of continuous data is that some studies do not report sufficient information to calculate the standard deviation (SDs) of the treatment effect. One of the approaches in handling this problem is through imputation. This article examines the empirical implications...

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Main Authors: Nik Idris, Nik Ruzni, Robertson, Chris
Format: Article
Language:English
Published: Taylor & Francis 2009
Subjects:
Online Access:http://irep.iium.edu.my/5530/
http://irep.iium.edu.my/5530/1/PUBLISHED_ARTICLE.pdf
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author Nik Idris, Nik Ruzni
Robertson, Chris
author_facet Nik Idris, Nik Ruzni
Robertson, Chris
author_sort Nik Idris, Nik Ruzni
building IIUM Repository
collection Online Access
description A common problem in the meta analysis of continuous data is that some studies do not report sufficient information to calculate the standard deviation (SDs) of the treatment effect. One of the approaches in handling this problem is through imputation. This article examines the empirical implications of imputing the missing SDs on the standard error (SE) of the overall meta analysis estimate. The simulation results show that if the SDs are missing under Missing Completely at Random and Missing at Random mechanism, imputation is recommended. With non random missing, imputation can lead to overestimation of the SE of the estimate.
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spelling iium-55302011-11-22T00:46:50Z http://irep.iium.edu.my/5530/ The effects of imputing the missing standard deviations on the standard error of meta analysis estimates Nik Idris, Nik Ruzni Robertson, Chris HA Statistics Q Science (General) A common problem in the meta analysis of continuous data is that some studies do not report sufficient information to calculate the standard deviation (SDs) of the treatment effect. One of the approaches in handling this problem is through imputation. This article examines the empirical implications of imputing the missing SDs on the standard error (SE) of the overall meta analysis estimate. The simulation results show that if the SDs are missing under Missing Completely at Random and Missing at Random mechanism, imputation is recommended. With non random missing, imputation can lead to overestimation of the SE of the estimate. Taylor & Francis 2009-03-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/5530/1/PUBLISHED_ARTICLE.pdf Nik Idris, Nik Ruzni and Robertson, Chris (2009) The effects of imputing the missing standard deviations on the standard error of meta analysis estimates. Communications in Statistics - Simulation and Computation, 38 (3). pp. 513-526. ISSN 0361-0918 (P), 1532-4141 (O) http://www.tandfonline.com/doi/pdf/10.1080/03610910802556106 10.1080/03610910802556106
spellingShingle HA Statistics
Q Science (General)
Nik Idris, Nik Ruzni
Robertson, Chris
The effects of imputing the missing standard deviations on the standard error of meta analysis estimates
title The effects of imputing the missing standard deviations on the standard error of meta analysis estimates
title_full The effects of imputing the missing standard deviations on the standard error of meta analysis estimates
title_fullStr The effects of imputing the missing standard deviations on the standard error of meta analysis estimates
title_full_unstemmed The effects of imputing the missing standard deviations on the standard error of meta analysis estimates
title_short The effects of imputing the missing standard deviations on the standard error of meta analysis estimates
title_sort effects of imputing the missing standard deviations on the standard error of meta analysis estimates
topic HA Statistics
Q Science (General)
url http://irep.iium.edu.my/5530/
http://irep.iium.edu.my/5530/
http://irep.iium.edu.my/5530/
http://irep.iium.edu.my/5530/1/PUBLISHED_ARTICLE.pdf