A Bayesian approach to assessing small‐study effects in meta‐analysis of a binary outcome with controlled false positive rate

Shi, Linyu; Chu, Haitao; Lin, Lifeng

doi:10.1002/jrsm.1415

Cited by 14 publications

(5 citation statements)

References 63 publications

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“…A comprehensive summary of the different settings in these programs in the future would be useful. Third, it is difficult for the one‐stage methods to test for the potential publication bias 63 ; the conventional methods for publication bias depend on study‐specific effects and cannot be directly applied to the one‐stage framework.…”

Section: Discussionmentioning

confidence: 99%

Synthesis of evidence from zero‐events studies: A comparison of one‐stage framework methods

Furuya‐Kanamori

Lin

2021

Research Synthesis Methods

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In evidence synthesis, dealing with zero‐events studies is an important and complicated task that has generated broad discussion. Numerous methods provide valid solutions to synthesizing data from studies with zero‐events, either based on a frequentist or a Bayesian framework. Among frequentist frameworks, the one‐stage methods have their unique advantages to deal with zero‐events studies, especially for double‐arm‐zero‐events. In this article, we give a concise overview of the one‐stage frequentist methods. We conducted simulation studies to compare the statistical properties of these methods to the two‐stage frequentist method (continuity correction) for meta‐analysis with zero‐events studies when double‐zero‐events studies were included. Our simulation studies demonstrated that the generalized estimating equation with unstructured correlation and beta‐binomial method had the best performance among the one‐stage methods. The random intercepts generalized linear mixed model showed good performance in the absence of obvious between‐study variance. Our results also showed that the continuity correction with inverse‐variance heterogeneous (IVhet) analytic model based on the two‐stage framework had good performance when the between‐study variance was obvious and the group size was balanced for included studies. In summary, the one‐stage framework has unique advantages to deal with studies with zero events and is not susceptive to group size ratio. It should be considered in future meta‐analyses whenever possible.

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Section: Discussionmentioning

confidence: 99%

Synthesis of evidence from zero‐events studies: A comparison of one‐stage framework methods

Furuya‐Kanamori

Lin

2021

Research Synthesis Methods

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“…The Egger test is a method used for detecting publication bias. It consists of a simple linear regression of the effect size across studies divided by the standard error versus the inverse of the standard error then testing (Palmer et al, 2008;L. Shi et al, 2020;X.…”

Section: Discussionmentioning

confidence: 99%

“…This research also detects publication bias with the Trim and Fill method. The method will remove outlier research, the smallest or the most extreme from the positive side of the funnel plot, and recalculate it so that the funnel plot is symmetrical L. Shi et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

The Influence of Teacher Efficacy on Education Quality: A Meta-Analysis

Hidayah¹,

Nur²,

Wuryandani³

et al. 2023

INT J EDUC METHODOL

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<p style="text-align: justify;">This research aims to prove the influence of teacher efficacy on learning quality with quantitative meta-analysis. The eligibility criteria in this study include: (a) The publication can be searched in Google Scholar, ERIC, DOAJ, Research Gate, and or ScienceDirect; (b) The publication is indexed in Scopus, WoS, SINTA (a portal indexing journal managed by the Ministry of Education and Culture of the Republic of Indonesia, equivalent to DOAJ and Index Copernicus), DOAJ, Index Copernicus, and at least they must be indexed in Google Scholar; (c) The topic of the studies must be relevant; (d) The studies must be carried out in the 2014-2023 year range; (e) The publication must have a value of (r), (t) or (F); (f) The studies have a magnitude of N ≥ 20. This study used the JASP application for data analysis. The results showed that: (a) the 40 studies analyzed were heterogeneous and normally distributed; (b) the influence of teacher efficacy on education quality is classified as strong (p < 0.05; rRE = 0.800); (c) publication bias was not detected. This study concluded that there is a strong influence of teacher efficacy on education quality.</p>

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“…For example, the (log) OR's variance depends on the event counts, which are actually random variables instead of fixed variables. The (log) OR and variance are thus intrinsically associated, and such association could lead to non-negligible biases for small sample sizes and/or low even rates [82][83][84].…”

Section: Considering One-stage Methodsmentioning

confidence: 99%

Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy

Zhao

Lin

2021

BioMedInformatics

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Systematic reviews and meta-analyses have been increasingly used to pool research findings from multiple studies in medical sciences. The reliability of the synthesized evidence depends highly on the methodological quality of a systematic review and meta-analysis. In recent years, several tools have been developed to guide the reporting and evidence appraisal of systematic reviews and meta-analyses, and much statistical effort has been paid to improve their methodological quality. Nevertheless, many contemporary meta-analyses continue to employ conventional statistical methods, which may be suboptimal compared with several alternative methods available in the evidence synthesis literature. Based on a recent systematic review on COVID-19 in pregnancy, this article provides an overview of select good practices for performing meta-analyses from statistical perspectives. Specifically, we suggest meta-analysts (1) providing sufficient information of included studies, (2) providing information for reproducibility of meta-analyses, (3) using appropriate terminologies, (4) double-checking presented results, (5) considering alternative estimators of between-study variance, (6) considering alternative confidence intervals, (7) reporting prediction intervals, (8) assessing small-study effects whenever possible, and (9) considering one-stage methods. We use worked examples to illustrate these good practices. Relevant statistical code is also provided. The conventional and alternative methods could produce noticeably different point and interval estimates in some meta-analyses and thus affect their conclusions. In such cases, researchers should interpret the results from conventional methods with great caution and consider using alternative methods.

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A Bayesian approach to assessing small‐study effects in meta‐analysis of a binary outcome with controlled false positive rate

Cited by 14 publications

References 63 publications

Synthesis of evidence from zero‐events studies: A comparison of one‐stage framework methods

Synthesis of evidence from zero‐events studies: A comparison of one‐stage framework methods

The Influence of Teacher Efficacy on Education Quality: A Meta-Analysis

Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy

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