A comparison of analytic approaches for individual patient data meta-analyses with binary outcomes

Thomas, Doneal; Platt, Robert W.; Benedetti, Andrea

doi:10.1186/s12874-017-0307-7

Cited by 17 publications

(18 citation statements)

References 33 publications

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“…Kuss15 also explores the use of generalised linear mixed models in a simulation study, but here we explore in detail how variations of this type of model compare to each other. Thomas et al32 also examine 2 of the generalised linear mixed models that we consider in a simulation study (our models 4 and 5) and conclude that the results using these models are “not importantly different.” We will see below that this is also the case in our simulation study; hence, our findings are in agreement with this previous finding.…”

Section: Simulation Studymentioning

confidence: 97%

A comparison of seven random‐effects models for meta‐analyses that estimate the summary odds ratio

Jackson

Law

Stijnen

et al. 2018

Statistics in Medicine

160

271

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Comparative trials that report binary outcome data are commonly pooled in systematic reviews and meta-analyses. This type of data can be presented as a series of 2-by-2 tables. The pooled odds ratio is often presented as the outcome of primary interest in the resulting meta-analysis. We examine the use of 7 models for random-effects meta-analyses that have been proposed for this purpose. The first of these models is the conventional one that uses normal within-study approximations and a 2-stage approach. The other models are generalised linear mixed models that perform the analysis in 1 stage and have the potential to provide more accurate inference. We explore the implications of using these 7 models in the context of a Cochrane Review, and we also perform a simulation study.We conclude that generalised linear mixed models can result in better statistical inference than the conventional 2-stage approach but also that this type of model presents issues and difficulties. These challenges include more demanding numerical methods and determining the best way to model study specific baseline risks. One possible approach for analysts is to specify a primary model prior to performing the systematic review but also to present the results using other models in a sensitivity analysis. Only one of the models that we investigate is found to perform poorly so that any of the other models could be considered for either the primary or the sensitivity analysis.

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Section: Simulation Studymentioning

confidence: 97%

A comparison of seven random‐effects models for meta‐analyses that estimate the summary odds ratio

Jackson

Law

Stijnen

et al. 2018

Statistics in Medicine

160

271

View full text Add to dashboard Cite

show abstract

“…Researchers have proposed several solutions to this bias, for example, the penalized likelihood [39]. However, these methods have been verified to be not superior to the standard likelihood estimation [40] and the GLMMs would make no sense when the total events in either of the groups were zero. The beta-binomial model may be promising solutions for meta-analyses with insufficient number of total events [16,17, 41, 42].…”

Section: Discussionmentioning

confidence: 99%

The impact of studies with no events in both arms on meta-analysis of rare events: a simulation study using generalized linear mixed model

Lin

2021

Preprint

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ObjectiveThe common approach to meta-analysis with double-zero studies is to remove such studies. Our previous work has confirmed that exclusion of these studies may impact the results. In this study, we undertook extensive simulations to investigate how the results of meta-analyses would be impacted in relation to the proportion of such studies.MethodsTwo standard generalized linear mixed models (GLMMs) were employed for the meta-analysis. The statistical properties of the two GLMMs were first examined in terms of percentage bias, mean squared error, and coverage. We then repeated all the meta-analyses after excluding double-zero studies. Direction of estimated effects and p-values for including against excluding double-zero studies were compared in nine ascending groups classified by the proportion of double-zero studies within a meta-analysis.ResultsBased on 50,000 simulated meta-analyses, the two GLMMs almost achieved unbiased estimation and reasonable coverage in most of the situations. When excluding double-zero studies, 0.00% to 4.47% of the meta-analyses changed the direction of effect size, and 0.61% to 8.78% changed direction of the significance of p-value. When the proportion of double-zero studies increased in a meta-analysis, the probability of the effect size changed the direction increased; when the proportion was about 40% to 60%, it has the largest impact on the change of p-values.ConclusionDouble-zero studies can impact the results of meta-analysis and excluding them may be problematic. The impact of such studies on meta-analysis varies by the proportion of such studies within a meta-analysis.

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“…Extensive guidance has previously been provided for conducting an IPD‐MA of intervention effects, for various types of outcome data, such as binary, continuous, ordinal and count outcomes. Yet, IPD‐MA are especially useful when analyzing time‐to‐event outcomes in intervention studies, as censored outcomes can be reassessed for the meta‐analysis, survival measures (eg, hazard ratios, median survival) can be calculated directly and independent to trial reporting, follow‐up length can often be increased, time‐varying hazard ratios can be examined, and effect modifiers (intervention‐covariate interactions) can be assessed.…”

Section: Introductionmentioning

confidence: 99%

Individual participant data meta‐analysis of intervention studies with time‐to‐event outcomes: A review of the methodology and an applied example

Jong

Moons

Riley

et al. 2020

Research Synthesis Methods

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Many randomized trials evaluate an intervention effect on time‐to‐event outcomes. Individual participant data (IPD) from such trials can be obtained and combined in a so‐called IPD meta‐analysis (IPD‐MA), to summarize the overall intervention effect. We performed a narrative literature review to provide an overview of methods for conducting an IPD‐MA of randomized intervention studies with a time‐to‐event outcome. We focused on identifying good methodological practice for modeling frailty of trial participants across trials, modeling heterogeneity of intervention effects, choosing appropriate association measures, dealing with (trial differences in) censoring and follow‐up times, and addressing time‐varying intervention effects and effect modification (interactions).We discuss how to achieve this using parametric and semi‐parametric methods, and describe how to implement these in a one‐stage or two‐stage IPD‐MA framework. We recommend exploring heterogeneity of the effect(s) through interaction and non‐linear effects. Random effects should be applied to account for residual heterogeneity of the intervention effect. We provide further recommendations, many of which specific to IPD‐MA of time‐to‐event data from randomized trials examining an intervention effect.We illustrate several key methods in a real IPD‐MA, where IPD of 1225 participants from 5 randomized clinical trials were combined to compare the effects of Carbamazepine and Valproate on the incidence of epileptic seizures.

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A comparison of analytic approaches for individual patient data meta-analyses with binary outcomes

Cited by 17 publications

References 33 publications

A comparison of seven random‐effects models for meta‐analyses that estimate the summary odds ratio

A comparison of seven random‐effects models for meta‐analyses that estimate the summary odds ratio

The impact of studies with no events in both arms on meta-analysis of rare events: a simulation study using generalized linear mixed model

Individual participant data meta‐analysis of intervention studies with time‐to‐event outcomes: A review of the methodology and an applied example

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