A new approach to outliers in meta-analysis

Baker, Rose; Jackson, Dan

doi:10.1007/s10729-007-9041-8

Cited by 66 publications

(87 citation statements)

References 21 publications

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“…Rabiee et al (2004Rabiee et al ( , 2005 used this method in exploring sources of heterogeneity in the controlled internal drug-releasing insert (CIDR) and Ovsynch data. Baker and Jackson (2008) have proposed a method that reduces the weight of studies that are outliers in meta-analyses. All of these methods for examining heterogeneity have merit, and the variety of methods available reflects the importance of this activity.…”

Section: Approaches To Heterogeneitymentioning

confidence: 99%

Invited review: Use of meta-analysis in animal health and reproduction: Methods and applications

Lean¹,

Rabiee

Duffield

et al. 2009

Journal of Dairy Science

205

173

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The objectives of this paper are to provide an introduction to meta-analysis and systematic review and to discuss the rationale for this type of research and other general considerations. We highlight methods used to produce a rigorous meta-analysis and discuss some aspects of interpretation of meta-analysis drawing on examples from the animal and veterinary science literature. Meta-analysis is a rapidly expanding area of research that has been relatively underutilized in animal and veterinary science. It is a quantitative, formal, epidemiological study design used to systematically assess previous research studies to derive conclusions about that body of research. Outcomes from a meta-analysis may include a more precise estimate of the effect of treatment or risk factor for disease, or other outcomes, than any individual study contributing to the pooled analysis. The examination of variability or heterogeneity in study results is also a critical outcome. The benefits of meta-analysis include a consolidated and quantitative review of a large, and often complex, sometimes apparently conflicting, body of literature. Meta-analytic methods place less emphasis on dichotomous outcomes from null hypothesis significance testing and greater emphasis on determining the magnitude and the precision of an effect of interest. A substantial benefit of meta-analysis is the potential to investigate new hypotheses using existing data, both through the development of a priori hypotheses and by examination of the heterogeneity in study responses. The specification of the outcome and hypotheses that are tested is critical to the conduct of meta-analyses, as is a sensitive literature search. A failure to identify the majority of existing studies can lead to erroneous conclusions; however, there are methods of examining data to identify the potential for studies to be missing; for example, by the use of funnel plots. Many of the statistical methods to conduct meta-analysis are widely used. Bayesian methods are well suited to meta-analysis. The post-hoc methods used to evaluate heterogeneity and publication bias, which include the I 2 statistic, L'Abbé plots, Galbraith plots, Rosenthal's N, and influential study analysis are exclusively used in meta-analysis. Examples where meta-analyses have been repeated in animal science or veterinary medicine show good consistency in estimates of effect. Findings of studies to date have provided new understandings of rumen modifiers, milk fever, parasite control, mastitis, somatotropin, and reproductive manipulations. Rigorously conducted meta-analyses are useful tools to improve animal well-being and productivity. The need to integrate findings from many studies ensures that meta-analytic research is desirable and the large body of research now generated makes the conduct of this research feasible.

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Section: Approaches To Heterogeneitymentioning

confidence: 99%

Invited review: Use of meta-analysis in animal health and reproduction: Methods and applications

Lean¹,

Rabiee

Duffield

et al. 2009

Journal of Dairy Science

205

173

View full text Add to dashboard Cite

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“…The two data sets: CDP-choline for cognitive and behavioural disturbances, and Fluoride toothpaste for preventing dental caries have been previously analysed [4]. These provide contrasting examples.…”

Section: Resultsmentioning

confidence: 99%

“…Excluding trial results based on their findings is another source of bias however and the presence of unexplained and unusual study results places the statistician in an uncomfortable situation. Outliers in regression analysis have been widely researched [2,3], but research into outliers in meta-analysis is much more recent [4,5]. If any outliers contribute very little weight to the analysis, because the studies that provide them are small for example, the choice between their inclusion or exclusion is moot; 'outlier' is not synonymous with 'influential'.…”

Section: Introductionmentioning

confidence: 99%

A random effects variance shift model for detecting and accommodating outliers in meta-analysis

Gumedze

Jackson

2011

BMC Med Res Methodol

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BackgroundMeta-analysis typically involves combining the estimates from independent studies in order to estimate a parameter of interest across a population of studies. However, outliers often occur even under the random effects model. The presence of such outliers could substantially alter the conclusions in a meta-analysis. This paper proposes a methodology for identifying and, if desired, downweighting studies that do not appear representative of the population they are thought to represent under the random effects model.MethodsAn outlier is taken as an observation (study result) with an inflated random effect variance. We used the likelihood ratio test statistic as an objective measure for determining whether observations have inflated variance and are therefore considered outliers. A parametric bootstrap procedure was used to obtain the sampling distribution of the likelihood ratio test statistics and to account for multiple testing. Our methods were applied to three illustrative and contrasting meta-analytic data sets.ResultsFor the three meta-analytic data sets our methods gave robust inferences when the identified outliers were downweighted.ConclusionsThe proposed methodology provides a means to identify and, if desired, downweight outliers in meta-analysis. It does not eliminate them from the analysis however and we consider the proposed approach preferable to simply removing any or all apparently outlying results. We do not however propose that our methods in any way replace or diminish the standard random effects methodology that has proved so useful, rather they are helpful when used in conjunction with the random effects model.

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“…Sensitivity analyses have also been used to examine the effects of studies identified as being aberrant concerning conduct or result, or being highly influential in the analysis. Recently, another method has been proposed that reduces the weight of studies that are outliers in meta-analyses 45 . All of these methods for examining heterogeneity have merit, and the variety of methods available reflects the importance of this activity.…”

Section: Negative Values Of Imentioning

confidence: 99%

Meta‐Analysis in Medical Research

Haidich

2005

278

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A new approach to outliers in meta-analysis

Cited by 66 publications

References 21 publications

Invited review: Use of meta-analysis in animal health and reproduction: Methods and applications

Invited review: Use of meta-analysis in animal health and reproduction: Methods and applications

A random effects variance shift model for detecting and accommodating outliers in meta-analysis

Meta‐Analysis in Medical Research

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