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Alternative measures of between-study heterogeneity in meta-analysis: Reducing the impact of outlying studies.

ABSTRACT: Meta-analysis has become a widely used tool to combine results from independent studies. The collected studies are homogeneous if they share a common underlying true effect size; otherwise, they are heterogeneous. A fixed-effect model is customarily used when the studies are deemed homogeneous, while a random-effects model is used for heterogeneous studies. Assessing heterogeneity in meta-analysis is critical for model selection and decision making. Ideally, if heterogeneity is present, it should permeate the entire collection of studies, instead of being limited to a small number of outlying studies. Outliers can have great impact on conventional measures of heterogeneity and the conclusions of a meta-analysis. However, no widely accepted guidelines exist for handling outliers. This article proposes several new heterogeneity measures. In the presence of outliers, the proposed measures are less affected than the conventional ones. The performance of the proposed and conventional heterogeneity measures are compared theoretically, by studying their asymptotic properties, and empirically, using simulations and case studies.

SUBMITTER: Lin L

PROVIDER: S-EPMC5106349 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Alternative measures of between-study heterogeneity in meta-analysis: Reducing the impact of outlying studies.

Lin Lifeng L Chu Haitao H Hodges James S JS

Biometrics 20160511 1

Meta-analysis has become a widely used tool to combine results from independent studies. The collected studies are homogeneous if they share a common underlying true effect size; otherwise, they are heterogeneous. A fixed-effect model is customarily used when the studies are deemed homogeneous, while a random-effects model is used for heterogeneous studies. Assessing heterogeneity in meta-analysis is critical for model selection and decision making. Ideally, if heterogeneity is present, it shoul ...[more]

PMID: 27167143

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Project description:BackgroundThe choice of outcome measure is a critical decision in the design of any clinical trial, but many Phase III clinical trials in critical care fail to detect a difference between the interventions being compared. This may be because the surrogate outcomes used to show beneficial effects in early phase trials (which informed the design of the subsequent Phase III trials) are not valid guides to the differences between the interventions for the main outcomes of the Phase III trials. We undertook a systematic review (1) to generate a list of outcome measures used in critical care trials, (2) to determine the variability in the outcome reporting in the respiratory subgroup and (3) to create a smaller list of potential early phase endpoints in the respiratory subgroup.MethodsData related to outcomes were extracted from studies published in the six top-ranked critical care journals between 2010 and 2020. Outcomes were classified into subcategories and categories. A subset of early phase endpoints relevant to the respiratory subgroup was selected for further investigation. The variability of the outcomes and the variability in reporting was investigated.ResultsA total of 6905 references were retrieved and a total of 294 separate outcomes were identified from 58 studies. The outcomes were then classified into 11 categories and 66 subcategories. A subset of 22 outcomes relevant for the respiratory group were identified as potential early phase outcomes. The summary statistics, time points and definitions show the outcomes are analysed and reported in different ways.ConclusionThe outcome measures were defined, analysed and reported in a variety of ways. This creates difficulties for synthesising data in systematic reviews and planning definitive trials. This review once again highlights an urgent need for standardisation and validation of surrogate outcomes reported in critical care trials. Future work should aim to validate and develop a core outcome set for surrogate outcomes in critical care trials.

Dataset Information

Alternative measures of between-study heterogeneity in meta-analysis: Reducing the impact of outlying studies.

Publications

Alternative measures of between-study heterogeneity in meta-analysis: Reducing the impact of outlying studies.

Similar Datasets

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