The Effects of Imputing the Missing Standard Deviations on the Standard Error of Meta Analysis Estimates

Authors: Idris, Nik Ruzni Nik¹; Robertson, Chris²

Source: Communications in Statistics: Simulation and Computation, Volume 38, Number 3, March 2009 , pp. 513-526(14)

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Abstract:

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.

Keywords: Imputation; MCAR; Meta analysis; Missing SDs; Standard error

Document Type: Research article

DOI: http://dx.doi.org/10.1080/03610910802556106

Affiliations: 1: Department of Computational and Theoretical Sciences, International Islamic University Malaysia, Kuantan, Pahang Malaysia 2: Department of Statistics and Modelling Sciences, University of Strathclyde, Glasgow, Scotland,Health Protection Scotland, Glasgow, Scotland

Publication date: 2009-03-01