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Bias Caused By Sampling Error In Meta-analysis With Small Sample Sizes

Title: Bias Caused By Sampling Error In Meta-analysis With Small Sample Sizes.
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Name(s): Lin, Lifeng, author
Type of Resource: text
Genre: Journal Article
Text
Journal Article
Date Issued: 2018-09-13
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: Background Meta-analyses frequently include studies with small sample sizes. Researchers usually fail to account for sampling error in the reported within-study variances; they model the observed study-specific effect sizes with the within-study variances and treat these sample variances as if they were the true variances. However, this sampling error may be influential when sample sizes are small. This article illustrates that the sampling error may lead to substantial bias in meta-analysis results. Methods We conducted extensive simulation studies to assess the bias caused by sampling error. Meta-analyses with continuous and binary outcomes were simulated with various ranges of sample size and extents of heterogeneity. We evaluated the bias and the confidence interval coverage for five commonly-used effect sizes (i.e., the mean difference, standardized mean difference, odds ratio, risk ratio, and risk difference). Results Sampling error did not cause noticeable bias when the effect size was the mean difference, but the standardized mean difference, odds ratio, risk ratio, and risk difference suffered from this bias to different extents. The bias in the estimated overall odds ratio and risk ratio was noticeable even when each individual study had more than 50 samples under some settings. Also, Hedges' g, which is a bias-corrected estimate of the standardized mean difference within studies, might lead to larger bias than Cohen's d in meta-analysis results. Conclusions Cautions are needed to perform meta-analyses with small sample sizes. The reported within-study variances may not be simply treated as the true variances, and their sampling error should be fully considered in such meta-analyses.
Identifier: FSU_libsubv1_wos_000444545800106 (IID), 10.1371/journal.pone.0204056 (DOI)
Keywords: difference, absolute risk, binary outcomes, detect publication bias, heterogeneity variance, odds ratio, random-effects models, rare events, relative risk, sparse data
Publication Note: The publisher’s version of record is available at https://doi.org/10.1371/journal.pone.0204056
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_libsubv1_wos_000444545800106
Owner Institution: FSU
Is Part Of: Plos One.
1932-6203
Issue: iss. 9, vol. 13

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Lin, L. (2018). Bias Caused By Sampling Error In Meta-analysis With Small Sample Sizes. Plos One. Retrieved from http://purl.flvc.org/fsu/fd/FSU_libsubv1_wos_000444545800106