Research Synthesis Methods最新文献

A recent paper proposed an alternative weighting scheme when performing matching-adjusted indirect comparisons. This alternative approach follows the conventional one in matching the covariate means across two studies but differs in that it maximizes the effective sample size when doing so. The appendix of this paper showed, assuming there is one covariate and negative weights are permitted, that the resulting weights are linear in the covariates. This explains how the alternative method achieves a larger effective sample size and results in a metric that quantifies the difficulty of matching on particular covariates. We explain how these key results generalize to the case where there are multiple covariates, giving rise to a new metric that can be used to quantify the impact of matching on multiple covariates.

Meta-analyses have become the gold standard for synthesizing evidence from multiple clinical trials, and they are especially useful when outcomes are rare or adverse since individual trials often lack sufficient power to detect a treatment effect. However, when zero events are observed in one or both treatment arms in a trial, commonly used meta-analysis methods can perform poorly. Continuity corrections (CCs), and numerical adjustments to the data to make computations feasible, have been proposed to ameliorate this issue. While the impact of various CCs on meta-analyses with rare events has been explored, how this impact varies based on the choice of pooling method and heterogeneity variance estimator is not widely understood. We compare several correction methods via a simulation study with a variety of commonly used meta-analysis methods. We consider how these method combinations impact important meta-analysis results, such as the estimated overall treatment effect, 95% confidence interval coverage, and Type I error rate. We also provide a website application of these results to aid researchers in selecting meta-analysis methods for rare-event data sets. Overall, no one-method combination can be consistently recommended, but some general trends are evident. For example, when there is no heterogeneity variance, we find that all pooling methods can perform well when paired with a specific correction method. Additionally, removing studies with zero events can work very well when there is no heterogeneity variance, while excluding single-zero studies results in poorer method performance when there is non-negligible heterogeneity variance and is not recommended.

In any meta-analysis, it is critically important to report the dispersion in effects as well as the mean effect. If an intervention has a moderate clinical impact on average we also need to know if the impact is moderate for all relevant populations, or if it varies from trivial in some to major in others. Or indeed, if the intervention is beneficial in some cases but harmful in others. Researchers typically report a series of statistics such as the Q-value, the p-value, and I², which are intended to address this issue. Often, they use these statistics to classify the heterogeneity as being low, moderate, or high and then use these classifications when considering the potential utility of the intervention. While this practice is ubiquitous, it is nevertheless incorrect. The statistics mentioned above do not actually tell us how much the effect size varies. Classifications of heterogeneity based on these statistics are uninformative at best, and often misleading. My goal in this paper is to explain what these statistics do tell us, and that none of them tells us how much the effect size varies. Then I will introduce the prediction interval, the statistic that does tell us how much the effect size varies, and that addresses the question we have in mind when we ask about heterogeneity. This paper is adapted from a chapter in “Common Mistakes in Meta-Analysis and How to Avoid Them.” A free PDF of the book is available at https://www.Meta-Analysis.com/rsm.

The number of meta-analyses of aggregate data has dramatically increased due to the facility of obtaining data from publications and the development of free, easy-to-use, and specialised statistical software. Even when meta-analyses include the same studies, their results may vary owing to different methodological choices. Assessment of the replication of meta-analysis provides an example of the variation of effect ‘naturally’ observed between multiple research projects. Reproducibility of results has mostly been reported using graphical descriptive representations. A quantitative analysis of such results would enable (i) breakdown of the total observed variability with quantification of the variability generated by the replication process and (ii) identification of which variables account for this variability, such as methodological quality or the statistical analysis procedures used. These variables might explain systematic mean differences between results and dispersion of the results. To quantitatively characterise the reproducibility of meta-analysis results, a bivariate linear mixed-effects model was developed to simulate both mean results and their corresponding uncertainty. Results were assigned to several replication groups, those assessing the same studies, outcomes, treatment indication and comparisons classified in the same replication group. A nested random effect structure was used to break down the total variability within each replication group and between these groups to enable calculation of an intragroup correlation coefficient and quantification of reproducibility. Determinants of variability were investigated by modelling both mean and variance parameters using covariates. The proposed model was applied to the example of meta-analyses evaluating direct oral anticoagulants in the acute treatment of venous thromboembolism.

Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment-covariate interactions in IPD meta-analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta-analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment-covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta-analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two-way interaction between treatment and an additional covariate (3/100); and (4) Three-way interaction model: Three-way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta-analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice.

A systematic review is a type of literature review that aims to collect and analyse all available evidence from the literature on a particular topic. The process of screening and identifying eligible articles from the vast amounts of literature is a time-consuming task. Specialised software has been developed to aid in the screening process and save significant time and labour. However, the most suitable software tools that are available often come with a cost or only offer either a limited or a trial version for free. In this paper, we report the release of a new software application, Catchii, which contains all the important features of a systematic review screening application while being completely free. It supports a user at different stages of screening, from detecting duplicates to creating the final flowchart for a publication. Catchii is designed to provide a good user experience and streamline the screening process through its clean and user-friendly interface on both computers and mobile devices. All in all, Catchii is a valuable addition to the current selection of systematic review screening applications. It enables researchers without financial resources to access features found in the best paid tools, while also diminishing costs for those who have previously relied on paid applications. Catchii is available at https://catchii.org.

Meta-analyses of treatment effects in randomized control trials are often faced with the problem of missing information required to calculate effect sizes and their sampling variances. Particularly, correlations between pre- and posttest scores are frequently not available. As an ad-hoc solution, researchers impute a constant value for the missing correlation. As an alternative, we propose adopting a multivariate meta-regression approach that models independent group effect sizes and accounts for the dependency structure using robust variance estimation or three-level modeling. A comprehensive simulation study mimicking realistic conditions of meta-analyses in clinical and educational psychology suggested that imputing a fixed correlation 0.8 or adopting a multivariate meta-regression with robust variance estimation work well for estimating the pooled effect but lead to slightly distorted between-study heterogeneity estimates. In contrast, three-level meta-regressions resulted in largely unbiased fixed effects but more inconsistent prediction intervals. Based on these results recommendations for meta-analytic practice and future meta-analytic developments are provided.

Searching for trials is a key task in systematic reviews and a focus of automation. Previous approaches required knowing examples of relevant trials in advance, and most methods are focused on published trial articles. To complement existing tools, we compared methods for finding relevant trial registrations given a International Prospective Register of Systematic Reviews (PROSPERO) entry and where no relevant trials have been screened for inclusion in advance. We compared SciBERT-based (extension of Bidirectional Encoder Representations from Transformers) PICO extraction, MetaMap, and term-based representations using an imperfect dataset mined from 3632 PROSPERO entries connected to a subset of 65,662 trial registrations and 65,834 trial articles known to be included in systematic reviews. Performance was measured by the median rank and recall by rank of trials that were eventually included in the published systematic reviews. When ranking trial registrations relative to PROSPERO entries, 296 trial registrations needed to be screened to identify half of the relevant trials, and the best performing approach used a basic term-based representation. When ranking trial articles relative to PROSPERO entries, 162 trial articles needed to be screened to identify half of the relevant trials, and the best-performing approach used a term-based representation. The results show that MetaMap and term-based representations outperformed approaches that included PICO extraction for this use case. The results suggest that when starting with a PROSPERO entry and where no trials have been screened for inclusion, automated methods can reduce workload, but additional processes are still needed to efficiently identify trial registrations or trial articles that meet the inclusion criteria of a systematic review.

Meta-analyses can be compromised by studies' internal biases (e.g., confounding in nonrandomized studies) as well as publication bias. These biases often operate nonadditively: publication bias that favors significant, positive results selects indirectly for studies with more internal bias. We propose sensitivity analyses that address two questions: (1) “For a given severity of internal bias across studies and of publication bias, how much could the results change?”; and (2) “For a given severity of publication bias, how severe would internal bias have to be, hypothetically, to attenuate the results to the null or by a given amount?” These methods consider the average internal bias across studies, obviating specifying the bias in each study individually. The analyst can assume that internal bias affects all studies, or alternatively that it only affects a known subset (e.g., nonrandomized studies). The internal bias can be of unknown origin or, for certain types of bias in causal estimates, can be bounded analytically. The analyst can specify the severity of publication bias or, alternatively, consider a “worst-case” form of publication bias. Robust estimation methods accommodate non-normal effects, small meta-analyses, and clustered estimates. As we illustrate by re-analyzing published meta-analyses, the methods can provide insights that are not captured by simply considering each bias in turn. An R package implementing the methods is available (multibiasmeta).

Interrupted time series (ITS) are often meta-analysed to inform public health and policy decisions but examination of the statistical methods for ITS analysis and meta-analysis in this context is limited. We simulated meta-analyses of ITS studies with continuous outcome data, analysed the studies using segmented linear regression with two estimation methods [ordinary least squares (OLS) and restricted maximum likelihood (REML)], and meta-analysed the immediate level- and slope-change effect estimates using fixed-effect and (multiple) random-effects meta-analysis methods. Simulation design parameters included varying series length; magnitude of lag-1 autocorrelation; magnitude of level- and slope-changes; number of included studies; and, effect size heterogeneity. All meta-analysis methods yielded unbiased estimates of the interruption effects. All random effects meta-analysis methods yielded coverage close to the nominal level, irrespective of the ITS analysis method used and other design parameters. However, heterogeneity was frequently overestimated in scenarios where the ITS study standard errors were underestimated, which occurred for short series or when the ITS analysis method did not appropriately account for autocorrelation. The performance of meta-analysis methods depends on the design and analysis of the included ITS studies. Although all random effects methods performed well in terms of coverage, irrespective of the ITS analysis method, we recommend the use of effect estimates calculated from ITS methods that adjust for autocorrelation when possible. Doing so will likely to lead to more accurate estimates of the heterogeneity variance.