Firstly, we use a simulation study to examine the coverage performance of the confidence and prediction intervals for a number of different methods. The approach allows for unexplained between‐study heterogeneity in the true treatment effect by incorporating random study effects about the overall mean There are numerous methods for constructing confidence intervals for a random effects meta‐analysis, and several articles have examined the performance of these methods. The performance of the random effects models in such situations should be the subject of further, more‐tailored simulation studies.Our simulation study also considered the impact of different mixtures of sample sizes on the model performance. The KR method generates a particularly erratic and uninformative prediction interval. The parameter values are summarised in Table In addition to the situation where all studies are of the same size (balanced: A systematic review and meta-analysis, Methods to calculate uncertainty in the estimated overall effect size from a random‐effects meta‐analysis, Simulation-based power calculations for planning a two-stage individual participant data meta-analysis, Should surgical ex vivo lymphadenectomy be a standard procedure in the management of patients with gastric cancer?, Is Clinical Anxiety a Risk or a Protective Factor for Executive Functioning in Youth with ADHD? The random-effects method (DerSimonian 1986) incorporates an assumption that the different studies are estimating different, yet related, intervention effects. As described in Section 9.4.3.1, the method is based on the inverse-variance approach, making an adjustment to the study weights according to the extent of variation, or heterogeneity, among the varying intervention effects.

, Exercise and cancer-related fatigue in adults: a systematic review of previous systematic reviews with meta-analyses, Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice, Inter‐individual differences in body mass index were not observed as a result of aerobic exercise in children and adolescents with overweight and obesity, Physical exercise and epicardial adipose tissue: A systematic review and meta‐analysis of randomized controlled trials, Moreover, for binary outcomes in general, the standard error will be strongly correlated with the effect estimate. A Meta-Analytic Perspective, Task sharing with non-physician health-care workers for management of blood pressure in low-income and middle-income countries: a systematic review and meta-analysis, Role of autobiographical memory in patient response to cognitive behavioural therapies for depression: protocol of an individual patient data meta-analysis, A comparison of the statistical performance of different meta-analysis models for the synthesis of subgroup effects from randomized clinical trials, Likelihood-based random-effects meta-analysis with few studies: empirical and simulation studies, Meta‐analysis of the effect of chilling on selected attributes of fresh pork, Is Viewing Mass Trauma Television Coverage Associated With Trauma Reactions in Adults and Youth? Correspondence to: Christopher Partlett, National Perinatal Epidemiology Unit, Richard Doll Building, Old Road Campus, Headington, Oxford, OX3 7LF, U.K.Research Institute for Primary Care and Health Sciences, Keele University, Keele, U.K.Correspondence to: Christopher Partlett, National Perinatal Epidemiology Unit, Richard Doll Building, Old Road Campus, Headington, Oxford, OX3 7LF, U.K.Research Institute for Primary Care and Health Sciences, Keele University, Keele, U.K.Use the link below to share a full-text version of this article with your friends and colleagues. Examine sources of between-study heterogeneity, e.g. For example, in Table Of all the modified methods, the HK method is frequently the best in terms of confidence interval coverage; however, when the heterogeneity is small and the study sizes are mixed, the HK method produces confidence intervals that are too wide. A random-effects meta-analysis reveals a statistically significant benefit on average, based on the inference in equation (13) regarding μ alone.

The approach allows for unexplained between‐study heterogeneity in the true treatment effect by incorporating random study effects about the overall mean. Interest may be in estimating the overall mean (summary or … This is caused by an unusually small value for the degrees of freedom More importantly, based on the findings of the simulation study, given that the relative degree of heterogeneity is small, it is likely that (aside from the erratic prediction interval for the KR method) all the derived prediction intervals are too narrow and thus inaccurate (see the bottom right of Table Because heterogeneity is large in this meta‐analysis, a prediction interval may be a more appropriate summary than the confidence interval for the mean effect; in other words, it is of interest whether the treatment effect is likely to always be beneficial in new populations.

Cornell However, when there is substantial heterogeneity between studies, prediction intervals are also useful for interpreting the results of a random effects meta‐analysis In this paper, we build upon these previous studies by taking a more in depth look into the performance of several different methods for constructing confidence intervals for the mean effect and, in particular, prediction intervals for the effect in a new setting, following REML estimation of the random effects model in a frequentist framework. However, as our study did not modulate the within or between study variance to account for this, we were unable to control degree of heterogeneity in these cases.Further, while we considered a broad range of values for Another limitation of this study is that we only consider estimating Another important issue when conducting a random effects meta‐analysis is the specification of the random effects distribution.

fixed effect or random effects meta-analysis.