Higher‐order likelihood inference in meta‐analysis and meta‐regression

Guolo, Annamaria

doi:10.1002/sim.4451

Cited by 45 publications

(87 citation statements)

References 39 publications

(52 reference statements)

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“…However, the PE method can only be performed when the number of studies is larger than five, whereas many meta-analyses are smaller [15]. Several other methods have been developed, like the Quantile Approximation (QA) method [28], the Profile Likelihood approach [29], natural weighting instead of empirically based weighting of studies [30], use of fixed effects estimates with a random effects approach to heterogeneity [31] and more recently, higher-order likelihood inference methods [32]. However, most of these methods are based on asymptotic statistics and they may therefore be less robust in case of a limited number of trials, or they remain difficult to use in practice, because no statistical packages are available to perform them and it is very difficult to carry out the calculations with standard software.…”

Section: Discussionmentioning

confidence: 99%

The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method

IntHout

Ioannidis

Borm

2014

BMC Med Res Methodol

1,392

1,061

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BackgroundThe DerSimonian and Laird approach (DL) is widely used for random effects meta-analysis, but this often results in inappropriate type I error rates. The method described by Hartung, Knapp, Sidik and Jonkman (HKSJ) is known to perform better when trials of similar size are combined. However evidence in realistic situations, where one trial might be much larger than the other trials, is lacking. We aimed to evaluate the relative performance of the DL and HKSJ methods when studies of different sizes are combined and to develop a simple method to convert DL results to HKSJ results.MethodsWe evaluated the performance of the HKSJ versus DL approach in simulated meta-analyses of 2–20 trials with varying sample sizes and between-study heterogeneity, and allowing trials to have various sizes, e.g. 25% of the trials being 10-times larger than the smaller trials. We also compared the number of “positive” (statistically significant at p < 0.05) findings using empirical data of recent meta-analyses with > = 3 studies of interventions from the Cochrane Database of Systematic Reviews.ResultsThe simulations showed that the HKSJ method consistently resulted in more adequate error rates than the DL method. When the significance level was 5%, the HKSJ error rates at most doubled, whereas for DL they could be over 30%. DL, and, far less so, HKSJ had more inflated error rates when the combined studies had unequal sizes and between-study heterogeneity. The empirical data from 689 meta-analyses showed that 25.1% of the significant findings for the DL method were non-significant with the HKSJ method. DL results can be easily converted into HKSJ results.ConclusionsOur simulations showed that the HKSJ method consistently results in more adequate error rates than the DL method, especially when the number of studies is small, and can easily be applied routinely in meta-analyses. Even with the HKSJ method, extra caution is needed when there are = <5 studies of very unequal sizes.

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Section: Discussionmentioning

confidence: 99%

The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method

IntHout

Ioannidis

Borm

2014

BMC Med Res Methodol

1,392

1,061

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“…For example, empirical coverages of confidence intervals are lower than the nominal level, and hypothesis tests can result in erroneous conclusions. In meta-analysis, recent works investigate the inaccuracy of first-order likelihood solutions when the sample size is small 10,18 and when the sample size within each study included in the meta-analysis is small as well. 19 When the reduced sample size cannot guarantee accuracy of asymptotic normality, the routine use of r P is discouraged and alternative solutions have been developed.…”

Section: First-order and Higher-order Likelihood Inferencementioning

confidence: 99%

“…For the more general case of meta-analysis and meta-regression, results in Guolo 10 highlight the advantages of using r P in place of r P in terms of accuracy of inferential conclusions for small to moderate sample sizes.…”

Section: Examplementioning

confidence: 99%

“…21 This paper takes advantage of the starting results in Guolo 10 to extend the use of Skovgaard's statistic to the multivariate meta-analysis represented by control rate regression. Measurement errors on̂i and̂i are taken into account, but they do not substantially affect the feasibility of the approach.…”

Section: Skovgaard's Statistic In Control Rate Regressionmentioning

confidence: 99%

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Improving likelihood‐based inference in control rate regression

Guolo

2017

Statistics in Medicine

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Control rate regression is a diffuse approach to account for heterogeneity among studies in meta-analysis by including information about the outcome risk of patients in the control condition. Correcting for the presence of measurement error affecting risk information in the treated and in the control group has been recognized as a necessary step to derive reliable inferential conclusions. Within this framework, the paper considers the problem of small sample size as an additional source of misleading inference about the slope of the control rate regression. Likelihood procedures relying on first-order approximations are shown to be substantially inaccurate, especially when dealing with increasing heterogeneity and correlated measurement errors. We suggest to address the problem by relying on higher-order asymptotics. In particular, we derive Skovgaard's statistic as an instrument to improve the accuracy of the approximation of the signed profile log-likelihood ratio statistic to the standard normal distribution. The proposal is shown to provide much more accurate results than standard likelihood solutions, with no appreciable computational effort. The advantages of Skovgaard's statistic in control rate regression are shown in a series of simulation experiments and illustrated in a real data example. R code for applying first-and second-order statistic for inference on the slope on the control rate regression is provided.

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“…68 69 Therefore, to pool outcome data for trials that make direct comparisons between interventions and alternatives, we will use the likelihood profile approach. 70 We will pool cross-over trials with parallel design RCTs using methods outlined in the Cochrane handbook to derive effect estimates. 66 Specifically, we will perform a paired t test for each crossover trial if any of the following are available: (1) the individual participant data; (2) the mean and SD or SE of the participant-specific differences, and between the intervention and control measurement; (3) the mean difference (MD) and one of the following: (a) a t-statistic from a paired t test; (b) a p value from a paired t-test; (c) a CI from a paired analysis; or (4) a graph of measurements of the intervention arm and control arm from which we can extract individual data values, so long as the matched measurement for each individual can be identified.…”

Section: Direct Comparisons Meta-analysesmentioning

confidence: 99%

Management of chronic neuropathic pain: a protocol for a multiple treatment comparison meta-analysis of randomised controlled trials

et al. 2014

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IntroductionChronic neuropathic pain is associated with reduced health-related quality of life and substantial socioeconomic costs. Current research addressing management of chronic neuropathic pain is limited. No review has evaluated all interventional studies for chronic neuropathic pain, which limits attempts to make inferences regarding the relative effectiveness of treatments.Methods and analysisWe will conduct a systematic review of all randomised controlled trials evaluating therapies for chronic neuropathic pain. We will identify eligible trials, in any language, by a systematic search of CINAHL, EMBASE, MEDLINE, AMED, HealthSTAR, DARE, PsychINFO and the Cochrane Central Registry of Controlled Trials. Eligible trials will be: (1) enrol patients presenting with chronic neuropathic pain, and (2) randomise patients to alternative interventions (pharmacological or non-pharmacological) or an intervention and a control arm. Pairs of reviewers will, independently and in duplicate, screen titles and abstracts of identified citations, review the full texts of potentially eligible trials and extract information from eligible trials. We will use a modified Cochrane instrument to evaluate risk of bias of eligible studies, recommendations from the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) to inform the outcomes we will collect, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to evaluate our confidence in treatment effects. When possible, we will conduct: (1) in direct comparisons, a random-effects meta-analysis to establish the effect of reported therapies on patient-important outcomes; and (2) a multiple treatment comparison meta-analysis within a Bayesian framework to assess the relative effects of treatments. We will define a priori hypotheses to explain heterogeneity between studies, and conduct meta-regression and subgroup analyses consistent with the current best practices.Ethics and DisseminationWe do not require ethics approval for our proposed review. We will disseminate our findings through peer-reviewed publications and conference presentations.Trial registration numberPROSPERO (CRD42014009212).

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Higher‐order likelihood inference in meta‐analysis and meta‐regression

Cited by 45 publications

References 39 publications

The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method

The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method

Improving likelihood‐based inference in control rate regression

Management of chronic neuropathic pain: a protocol for a multiple treatment comparison meta-analysis of randomised controlled trials

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