ArticleBMC medical research methodology2016
Confidence intervals for the between-study variance in random-effects meta-analysis using generalised heterogeneity statistics: should we use unequal tails?
Article in BMC medical research methodology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 8 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 8 syntheses or guidelines pooled it.
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- Imprecision and Preferences in Interpretation of Verbal Probabilities in Health: a Systematic Review.Journal of general internal medicine · 2021Pooled it
- Study specific prediction intervals for random-effects meta-analysis: A tutorial: Prediction intervals in meta-analysis.Research synthesis methods · 2021Pooled it
- Thrombectomy for Ischemic Stroke Beyond 24 Hours: A Meta-Analysis.Life (Basel, Switzerland) · 2025Review
- Incidence of Adverse Drug Reactions Among HIV Patients on Antiretroviral Drugs in Ethiopia: A Systematic Review and Meta-Analysis.AIDS research and treatment · 2025Review
- Knowledge, attitudes, and practices towards Human Papilloma Virus and uptake of HPV vaccine: A protocol for a systematic review.PloS one · 2024Article
- Comparative efficacy of exercise therapy and oral non-steroidal anti-inflammatory drugs and paracetamol for knee or hip osteoarthritis: a network meta-analysis of randomised controlled trials.British journal of sports medicine · 2023Article
- Performance, Egg Quality, and Immunity of Laying Hens due to Natural Carotenoid Supplementation: A Meta-Analysis.Food science of animal resources · 2023Article
- Hypertension control rate in India: systematic review and meta-analysis of population-level non-interventional studies, 2001-2022.The Lancet regional health. Southeast Asia · 2023Article
- Jackknife empirical likelihood confidence intervals for assessing heterogeneity in meta-analysis of rare binary event data.Contemporary clinical trials · 2021Article
- Statistical Methods for Quantifying Between-study Heterogeneity in Meta-analysis with Focus on Rare Binary Events.Statistics and its interface · 2020Article
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Abstract
backgroundConfidence intervals for the between study variance are useful in random-effects meta-analyses because they quantify the uncertainty in the corresponding point estimates. Methods for calculating these confidence intervals have been developed that are based on inverting hypothesis tests using generalised heterogeneity statistics. Whilst, under the random effects model, these new methods furnish confidence intervals with the correct coverage, the resulting intervals are usually very wide, making them uninformative.
methodsWe discuss a simple strategy for obtaining 95 % confidence intervals for the between-study variance with a markedly reduced width, whilst retaining the nominal coverage probability. Specifically, we consider the possibility of using methods based on generalised heterogeneity statistics with unequal tail probabilities, where the tail probability used to compute the upper bound is greater than 2.5 %. This idea is assessed using four real examples and a variety of simulation studies. Supporting analytical results are also obtained.
resultsOur results provide evidence that using unequal tail probabilities can result in shorter 95 % confidence intervals for the between-study variance. We also show some further results for a real example that illustrates how shorter confidence intervals for the between-study variance can be useful when performing sensitivity analyses for the average effect, which is usually the parameter of primary interest.
conclusionsWe conclude that using unequal tail probabilities when computing 95 % confidence intervals for the between-study variance, when using methods based on generalised heterogeneity statistics, can result in shorter confidence intervals. We suggest that those who find the case for using unequal tail probabilities convincing should use the '1-4 % split', where greater tail probability is allocated to the upper confidence bound. The 'width-optimal' interval that we present deserves further investigation.
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