Meta-regression methods to characterize evidence strength using meaningful-effect percentages conditional on study characteristics

The economic rationale for cell-based influenza vaccines in children and adults: A review of cost-effectiveness analyses

Seasonal influenza significantly affects both health and economic costs in children and adults. This narrative review summarizes published cost-effectiveness analyses (CEAs) of cell-based influenza vaccines in children and adults <65 years of age, critically assesses the assumptions and approaches used in these analyses, and considers the role of cell-based influenza vaccines for children and adults. CEAs from multiple countries demonstrated the cost-effectiveness of cell-based quadrivalent influenza vaccines (QIVc) compared with egg-based trivalent/quadrivalent influenza vaccines (TIVe/QIVe). CEA findings were consistent across models relying on different relative vaccine effectiveness (rVE) estimate inputs, with the rVE of QIVc versus QIVe ranging from 8.1% to 36.2% in favor of QIVc. Across multiple scenarios and types of analyses, QIVc was consistently cost-effective compared with QIVe, including in children and adults across different regions of the world.

Evidence for Infant-directed Speech Preference Is Consistent Across Large-scale, Multi-site Replication and Meta-analysis

There is substantial evidence that infants prefer infant-directed speech (IDS) to adult-directed speech (ADS). The strongest evidence for this claim has come from two large-scale investigations: i) a community-augmented meta-analysis of published behavioral studies and ii) a large-scale multi-lab replication study. In this paper, we aim to improve our understanding of the IDS preference and its boundary conditions by combining and comparing these two data sources across key population and design characteristics of the underlying studies. Our analyses reveal that both the meta-analysis and multi-lab replication show moderate effect sizes (d ≈ 0.35 for each estimate) and that both of these effects persist when relevant study-level moderators are added to the models (i.e., experimental methods, infant ages, and native languages). However, while the overall effect size estimates were similar, the two sources diverged in the effects of key moderators: both infant age and experimental method predicted IDS preference in the multi-lab replication study, but showed no effect in the meta-analysis. These results demonstrate that the IDS preference generalizes across a variety of experimental conditions and sampling characteristics, while simultaneously identifying key differences in the empirical picture offered by each source individually and pinpointing areas where substantial uncertainty remains about the influence of theoretically central moderators on IDS preference. Overall, our results show how meta-analyses and multi-lab replications can be used in tandem to understand the robustness and generalizability of developmental phenomena.

Sensitivity analysis for the interactive effects of internal bias and publication bias in meta-analyses

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).

Do proton pump inhibitors increase the risk of dementia? A systematic review, meta-analysis and bias analysis

AIM

Previous studies on the association between proton pump inhibitor (PPI) intake and the increased risk of dementia has shown discrepancies in their conclusions. We aimed to provide updated evidence based on extensive bias assessments and quantitative sensitivity analyses.

METHODS

We searched the databases PubMed, EMBASE, SCOPUS, CENTRAL and clinicaltrials.gov for prospective studies that examined an association between PPI use and dementia, up to February 2022. Each study was assessed using the Cochrane risk of bias assessment tools for non-randomized studies of interventions (ROBINS-I) or randomized trials (RoB2). Pooled risk ratios (RRs) and 95% prediction intervals were computed using random-effects models. Sensitivity analyses were adjusted for small-study bias.

RESULTS

We included nine observational studies with 204 108 dementia cases in the primary analysis on the association between PPI use vs. non-use and dementia, and the RR was 1.16 (95% CI = 1.00; 1.35). After adjusting for small-study bias by Copas selection model and Rücker's shrinkage procedure, the RR was 1.16 (1.02; 1.32) and 1.15 (1.13; 1.17), respectively. A subgroup analysis of PPI use vs. non-use regarding Alzheimer's disease risk yielded an RR of 1.15 (0.89; 1.50). The secondary analysis on the risk of dementia by use of PPI vs. histamine-2 receptor antagonist showed an RR of 1.03 (0.66; 1.62).

CONCLUSION

This meta-analysis provided no clear evidence for an association between PPI intake and the risk of dementia. Due to discrepancies in sensitivity analyses, however, some risk of dementia by PPI use cannot be ruled out. Since an unequivocal conclusion is still pending, further research is warranted.

Methods to Address Confounding and Other Biases in Meta-Analyses: Review and Recommendations

Meta-analyses contribute critically to cumulative science, but they can produce misleading conclusions if their constituent primary studies are biased, for example by unmeasured confounding in nonrandomized studies. We provide practical guidance on how meta-analysts can address confounding and other biases that affect studies' internal validity, focusing primarily on sensitivity analyses that help quantify how biased the meta-analysis estimates might be. We review a number of sensitivity analysis methods to do so, especially recent developments that are straightforward to implement and interpret and that use somewhat less stringent statistical assumptions than do earlier methods. We give recommendations for how these newer methods could be applied in practice and illustrate using a previously published meta-analysis. Sensitivity analyses can provide informative quantitative summaries of evidence strength, and we suggest reporting them routinely in meta-analyses of potentially biased studies. This recommendation in no way diminishes the importance of defining study eligibility criteria that reduce bias and of characterizing studies' risks of bias qualitatively. Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.