The logic and role of meta-analysis in clinical research

Sandoz, Mélanie

doi:10.1177/096228029300200203

Cited by 26 publications

(1 citation statement)

References 21 publications

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“…One of the main traditional limitations of meta-analysis is the heterogeneity, “mixing apples and oranges”, issue [ 8 , 31 , 32 ]. However, exploring the possible reasons for heterogeneity between studies is an important aspect of conducting a meta-analysis [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Introducing Meta-Partition, a Useful Methodology to Explore Factors That Influence Ecological Effect Sizes

et al. 2016

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The study of the heterogeneity of effect sizes is a key aspect of ecological meta-analyses. Here we propose a meta-analytic methodology to study the influence of moderators in effect sizes by splitting heterogeneity: meta-partition. To introduce this methodology, we performed a meta-partition of published data about the traits that influence species sensitivity to habitat loss, that have been previously analyzed through meta-regression. Thus, here we aim to introduce meta-partition and to make an initial comparison with meta-regression. Meta-partition algorithm consists of three steps. Step 1 is to study the heterogeneity of effect sizes under the assumption of fixed effect model. If heterogeneity is found, we perform step 2, that is, to partition the heterogeneity by the moderator that minimizes heterogeneity within a subset while maximizing heterogeneity between subsets. Then, if effect sizes of the subset are still heterogeneous, we repeat step 1 and 2 until we reach final subsets. Finally, step 3 is to integrate effect sizes of final subsets, with fixed effect model if there is homogeneity, and with random effects model if there is heterogeneity. Results show that meta-partition is valuable to assess the importance of moderators in explaining heterogeneity of effect sizes, as well as to assess the directions of these relations and to detect possible interactions between moderators. With meta-partition we have been able to evaluate the importance of moderators in a more objective way than with meta-regression, and to visualize the complex relations that may exist between them. As ecological issues are often influenced by several factors interacting in complex ways, ranking the importance of possible moderators and detecting possible interactions would make meta-partition a useful exploration tool for ecological meta-analyses.

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Section: Discussionmentioning

confidence: 99%

Introducing Meta-Partition, a Useful Methodology to Explore Factors That Influence Ecological Effect Sizes

et al. 2016

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A Comparison of Meta-analytic Results Using Literature vs Individual Patient Data

Jeng

Scott²,

Burmeister³

1995

JAMA

106

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Meta‐Analysis in Clinical Risk Assessment

Goldberg

Watson

Friedman

2008

Encyclopedia of Quantitative Risk Analysis and Assessment

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Meta‐analysis is a statistical approach that provides a logical structure and a quantitative methodology for the review, evaluation, and synthesis of information from independent studies. Combining the results of multiple independent studies in a well‐conducted meta‐analysis provides an objective summary assessment of the overall results of a series of independent studies. With the use of meta‐analytic techniques, a series of clinical trials for example, can be summarized, and the overall treatment effect can be estimated with increased precision so that the risks of reaching incorrect conclusions from individual trials are reduced. In this article, we introduce the process of meta‐analysis, issues in the conduct of a meta‐analysis, and some statistical models that are commonly used to conduct a meta‐analysis.

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The logic and role of meta-analysis in clinical research

Cited by 26 publications

References 21 publications

Introducing Meta-Partition, a Useful Methodology to Explore Factors That Influence Ecological Effect Sizes

Introducing Meta-Partition, a Useful Methodology to Explore Factors That Influence Ecological Effect Sizes

A Comparison of Meta-analytic Results Using Literature vs Individual Patient Data

Meta‐Analysis in Clinical Risk Assessment

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