Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression

Kosmidis, Ioannis; Guolo, Annamaria; Varin, Cristiano

doi:10.1093/biomet/asx001

Cited by 14 publications

(44 citation statements)

References 29 publications

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“…79,[140][141][142] In such cases, a bias reduction approach is suggested. 143 Hence, the Bartlett-type correction (method 7) and the Skovgaard statistic (method 8) are the two main proposals for higher order approximations when using methods based on the PL. 79…”

Section: Hartung-knapp/sidik-jonkman Cis (Methods 4 and 5)mentioning

confidence: 99%

“…The PL method is often preferred to the WTz method, as it is associated with a higher coverage closer to the nominal level, even when k is relatively small. 122,143 Jackson et al 158 showed that the PL method performed well and better than the WTz and WTt methods in meta-analyses with few studies (k ≤ 8) with coverage close to the nominal level. However, coverage decreases as τ 2 increases and/or k decreases.…”

Section: Hksj Methods (Methods 4 and 5)mentioning

confidence: 99%

See 1 more Smart Citation

Methods to calculate uncertainty in the estimated overall effect size from a random‐effects meta‐analysis

Veroniki

Jackson

Bender

et al. 2018

Research Synthesis Methods

152

210

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Meta-analyses are an important tool within systematic reviews to estimate the overall effect size and its confidence interval for an outcome of interest. If heterogeneity between the results of the relevant studies is anticipated, then a random-effects model is often preferred for analysis. In this model, a prediction interval for the true effect in a new study also provides additional useful information. However, the DerSimonian and Laird method-frequently used as the default method for meta-analyses with random effects-has been long challenged due to its unfavorable statistical properties. Several alternative methods have been proposed that may have better statistical properties in specific scenarios. In this paper, we aim to provide a comprehensive overview of available methods for calculating point estimates, confidence intervals, and prediction intervals for the overall effect size under the random-effects model. We indicate whether some methods are preferable than others by considering the results of comparative simulation and real-life data studies.

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Section: Hartung-knapp/sidik-jonkman Cis (Methods 4 and 5)mentioning

confidence: 99%

Section: Hksj Methods (Methods 4 and 5)mentioning

confidence: 99%

Methods to calculate uncertainty in the estimated overall effect size from a random‐effects meta‐analysis

Veroniki

Jackson

Bender

et al. 2018

Research Synthesis Methods

152

210

View full text Add to dashboard Cite

show abstract

“…In addition the estimates of variance components have a relatively large bias when the sample size is small which is a common problem of MLEs in mixed effects models. The adjusted profiled likelihood (McCullagh and Tibshirani, 1990) or bias-reducing penalized likelihood (Kosmidis et al, 2015) may be used to reduce bias in the estimates of variance components. Involving integrations with respect to random effects, the marginal likelihood function is not guaranteed to be convex which make it very difficult to derive asymptotic properties.…”

Section: Resultsmentioning

confidence: 99%

Smoothing Spline Mixed-Effects Density Models for Clustered Data

Chiu¹,

Liu²,

Wang³

2020

STAT SINICA

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We propose smoothing spline mixed-effects density models for the nonparametric estimation of density and conditional density functions with clustered data. The random effects in a density model introduce within-cluster correlation and allow us to borrow strength across clusters by shrinking cluster specific density functions to the population average, where the amount of shrinkage is decided by data. Estimation is carried out using the penalized likelihood and computed using a Markov chain Monte Carlo stochastic approximation algorithm. We derive an approximate cross-validation estimate of the aggregated Kullback-Leibler loss for the selection of smoothing parameters. The simulation study indicates that the proposed estimation method performs well. We apply our methods to investigate the evolution of hemoglobin density functions over time in response to guideline changes on anemia management for dialysis patients.

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“…Nemes et al [ 41 ] show that logistic regression overestimates the odds ratio because of bias of order 1/ N in studies with small and moderate sample sizes. Kosmidis et al [ 42 ] studied bias of order 1/ N in the maximum-likelihood estimates of the overall effect measure and the between-study variance under the normal random-effects model. However, the transformation biases in the mixed effects models are of order 1, as discussed in [ 6 ].…”

Section: Discussionmentioning

confidence: 99%

Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study

Bakbergenuly

Kulinskaya

2018

BMC Med Res Methodol

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BackgroundSystematic reviews and meta-analyses of binary outcomes are widespread in all areas of application. The odds ratio, in particular, is by far the most popular effect measure. However, the standard meta-analysis of odds ratios using a random-effects model has a number of potential problems. An attractive alternative approach for the meta-analysis of binary outcomes uses a class of generalized linear mixed models (GLMMs). GLMMs are believed to overcome the problems of the standard random-effects model because they use a correct binomial-normal likelihood. However, this belief is based on theoretical considerations, and no sufficient simulations have assessed the performance of GLMMs in meta-analysis. This gap may be due to the computational complexity of these models and the resulting considerable time requirements.MethodsThe present study is the first to provide extensive simulations on the performance of four GLMM methods (models with fixed and random study effects and two conditional methods) for meta-analysis of odds ratios in comparison to the standard random effects model.ResultsIn our simulations, the hypergeometric-normal model provided less biased estimation of the heterogeneity variance than the standard random-effects meta-analysis using the restricted maximum likelihood (REML) estimation when the data were sparse, but the REML method performed similarly for the point estimation of the odds ratio, and better for the interval estimation.ConclusionsIt is difficult to recommend the use of GLMMs in the practice of meta-analysis. The problem of finding uniformly good methods of the meta-analysis for binary outcomes is still open.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0531-9) contains supplementary material, which is available to authorized users.

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Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression

Cited by 14 publications

References 29 publications

Methods to calculate uncertainty in the estimated overall effect size from a random‐effects meta‐analysis

Methods to calculate uncertainty in the estimated overall effect size from a random‐effects meta‐analysis

Smoothing Spline Mixed-Effects Density Models for Clustered Data

Meta-analysis of binary outcomes via generalized linear mixed models: a simulation study

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