2016
DOI: 10.1002/sim.7140
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Random effects meta‐analysis: Coverage performance of 95% confidence and prediction intervals following REML estimation

Abstract: A random effects meta‐analysis combines the results of several independent studies to summarise the evidence about a particular measure of interest, such as a treatment effect. The approach allows for unexplained between‐study heterogeneity in the true treatment effect by incorporating random study effects about the overall mean. The variance of the mean effect estimate is conventionally calculated by assuming that the between study variance is known; however, it has been demonstrated that this approach may be… Show more

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Cited by 147 publications
(160 citation statements)
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“…The prediction interval has been described as providing “potentially the most relevant and complete statistical inferences to be drawn from random effects meta‐analyses” . However, we exercise due caution in inferences drawn from the prediction interval given the coverage issues identified in the simulations conducted by Partlett and Riley . These authors reported that the coverage of the interval was particularly poor in cases of low effect heterogeneity and/or markedly variable sample size.…”
Section: Discussionmentioning
confidence: 98%
See 1 more Smart Citation
“…The prediction interval has been described as providing “potentially the most relevant and complete statistical inferences to be drawn from random effects meta‐analyses” . However, we exercise due caution in inferences drawn from the prediction interval given the coverage issues identified in the simulations conducted by Partlett and Riley . These authors reported that the coverage of the interval was particularly poor in cases of low effect heterogeneity and/or markedly variable sample size.…”
Section: Discussionmentioning
confidence: 98%
“…Such under‐coverage would have no material effect on the derived probability of individual response variance in a future trial being clinically relevant. However, we still consider it prudent to view our prediction interval as approximate, as recommended by Partlett and Riley .…”
Section: Discussionmentioning
confidence: 99%
“…The standard deviation of this prognostic factor effect across studies is denoted by τ, and non-zero values suggest there is between-study heterogeneity. Confidence intervals for µ should ideally account for uncertainty in estimated variances (in particular τ),51 and we have found the approach of Hartung-Knapp to be robust for this purpose in most settings 1652. When synthesising prognostic effects on the log scale, the summary results and confidence intervals require back transformation (using the exponential function) to the original scale.…”
Section: Step 5: Meta-analysismentioning
confidence: 99%
“…Besides reporting on the confidence intervals (CIs) which quantify the precision of an estimated effect, in the results we also report prediction intervals (PIs) which present the expected range of true study effects in similar, new studies (IntHout et al 2016). It can be particularly informative to inspect PIs when there is high heterogeneity between studies included in a meta-analysis (IntHout et al 2016;Partlett and Riley 2017). Subsequently, we ran separate analyses (QM tests; Viechtbauer 2010) for each EF including one moderator at a time.…”
Section: Meta-regression Proceduresmentioning
confidence: 99%