Simple Strategies for Variance Uncertainty in Meta-Analysis
type: CreativeWork
description: Reliability of the measure of precision of the estimate is crucial; a correct value of the standard error of the point estimate entails that the resulting signiﬁcance of the analysis is correctly stated and that conﬁdence intervals have correct coverage probabilities. Stating an incorrect precision, on the contrary, can often result in biased and misleading results. In particular, in ﬁxed-eﬀects meta-analysis the overall estimator usually used in practice tends to have a variance higher than the optimal one even though this appears to be lower, just by chance. In performing a ﬁxed-eﬀects meta-analysis, individual treatment estimates are weighted proportionately to the precision of the study. Such weighting is optimal only under the assumption that variances are known, which is never the case in practice. As a consequence, the estimator is sub-optimal and the resulting meta-analysis overstates the signiﬁcance of the results: in particular, overstatements are dramatic when we summarise studies with small number of patients. Focusing the attention to the ﬁxed-eﬀects model, the main aim of this thesis is to investigate the behaviour of the precision of the overall estimator under diﬀerent circumstances in order to assess how biased and incorrectly reported the overall variance of the commonly used estimator is and also to highlight in which circumstances improved estimates are deemed necessary. In ﬁxed-eﬀects meta-analysis, problems are related to poor estimates of the individual variancesσ2isince these values are imprecise and both θ (the point estimator) and V (the overall variance estimator) depend upon them. Poorly estimated study variances can lead to the overall estimate of the variance of the treatment eﬀect being badly underestimated. In order to evaluate the circumstances in which the imprecision in the estimates of σ2ibadly aﬀects V, a number of simulations in diﬀerent settings were performed. Under both the assumption of common and uncommon variance of the ob…
abstract: Reliability of the measure of precision of the estimate is crucial; a correct value of the standard error of the point estimate entails that the resulting signiﬁcance of the analysis is correctly stated and that conﬁdence intervals have correct coverage probabilities. Stating an incorrect precision, on the contrary, can often result in biased and misleading results. In particular, in ﬁxed-eﬀects meta-analysis the overall estimator usually used in practice tends to have a variance higher than the optimal one even though this appears to be lower, just by chance. In performing a ﬁxed-eﬀects meta-analysis, individual treatment estimates are weighted proportionately to the precision of the study. Such weighting is optimal only under the assumption that variances are known, which is never the case in practice. As a consequence, the estimator is sub-optimal and the resulting meta-analysis overstates the signiﬁcance of the results: in particular, overstatements are dramatic when we summarise studies with small number of patients. Focusing the attention to the ﬁxed-eﬀects model, the main aim of this thesis is to investigate the behaviour of the precision of the overall estimator under diﬀerent circumstances in order to assess how biased and incorrectly reported the overall variance of the commonly used estimator is and also to highlight in which circumstances improved estimates are deemed necessary. In ﬁxed-eﬀects meta-analysis, problems are related to poor estimates of the individual variancesσ2isince these values are imprecise and both θ (the point estimator) and V (the overall variance estimator) depend upon them. Poorly estimated study variances can lead to the overall estimate of the variance of the treatment eﬀect being badly underestimated. In order to evaluate the circumstances in which the imprecision in the estimates of σ2ibadly aﬀects V, a number of simulations in diﬀerent settings were performed. Under both the assumption of common and uncommon variance of the ob…
author: Di Gessa, Giorgio
language: eng
date published: 2007
date modified: 2026-07-24T02:44:31Z
provider: King's College London
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Glasgow