scholarly journals Fisher transformation based confidence intervals of correlations in fixed‐ and random‐effects meta‐analysis

2021 ◽  
Author(s):  
Thilo Welz ◽  
Philipp Doebler ◽  
Markus Pauly
Keyword(s):  
Confidence Intervals ◽  
Random Effects ◽  
Meta Analysis ◽  
Fixed And Random Effects

How Many Studies Do You Need?

2010 ◽  
Vol 35 (2) ◽  
pp. 215-247 ◽  
Author(s):  
Jeffrey C. Valentine ◽  
Therese D. Pigott ◽  
Hannah R. Rothstein
Keyword(s):  
Confidence Intervals ◽  
Random Effects ◽  
Power Analysis ◽  
Variance Component ◽  
Statistical Power ◽  
Meta Analysis ◽  
Inclusion Criteria ◽  
Fixed And Random Effects ◽  
Quantitative Synthesis ◽  
Power Analyses

In this article, the authors outline methods for using fixed and random effects power analysis in the context of meta-analysis. Like statistical power analysis for primary studies, power analysis for meta-analysis can be done either prospectively or retrospectively and requires assumptions about parameters that are unknown. The authors provide some suggestions for thinking about these parameters, in particular for the random effects variance component. The authors also show how the typically uninformative retrospective power analysis can be made more informative. The authors then discuss the value of confidence intervals, show how they could be used in addition to or instead of retrospective power analysis, and also demonstrate that confidence intervals can convey information more effectively in some situations than power analyses alone. Finally, the authors take up the question “How many studies do you need to do a meta-analysis?” and show that, given the need for a conclusion, the answer is “two studies,” because all other synthesis techniques are less transparent and/or are less likely to be valid. For systematic reviewers who choose not to conduct a quantitative synthesis, the authors provide suggestions for both highlighting the current limitations in the research base and for displaying the characteristics and results of studies that were found to meet inclusion criteria.

scholarly journals Association of bisphenol A with puberty timing: a meta-analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hui Meng ◽  
Yunping Zhou ◽  
Yunxia Jiang
Keyword(s):  
Bisphenol A ◽  
Confidence Intervals ◽  
Random Effects ◽  
Strong Correlation ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Random Effects Models ◽  
Study Heterogeneity ◽  
The Relationship ◽  
Prevalence Ratios

AbstractObjectivesThe results of existing studies on bisphenol A (BPA) and puberty timing did not reach a consensus. Thereby we performed this meta-analytic study to explore the association between BPA exposure in urine and puberty timing.MethodsMeta-analysis of the pooled odds ratios (OR), prevalence ratios (PR) or hazards ratios (HR) with 95% confidence intervals (CI) were calculated and estimated using fixed-effects or random-effects models based on between-study heterogeneity.ResultsA total of 10 studies involving 5621 subjects were finally included. The meta-analysis showed that BPA exposure was weakly associated with thelarche (PR: 0.96, 95% CI: 0.93–0.99), while no association was found between BPA exposure and menarche (HR: 0.99, 95% CI: 0.89–1.12; OR: 1.02, 95% CI: 0.73–1.43), and pubarche (OR: 1.00, 95% CI: 0.79–1.26; PR: 1.00, 95% CI: 0.95–1.05).ConclusionsThere was no strong correlation between BPA exposure and puberty timing. Further studies with large sample sizes are needed to verify the relationship between BPA and puberty timing.

EXPRESS: A meta-analysis of aspirin and subarachnoid haemorrhage in patients with intracranial aneurysms yields different results to the general population

2021 ◽  
pp. 174749302110048
Author(s):  
Frederick Ewbank ◽  
Jacqueline Birks ◽  
Diederik Bulters
Keyword(s):  
General Population ◽  
Subarachnoid Haemorrhage ◽  
Confidence Intervals ◽  
Random Effects ◽  
Intracranial Aneurysms ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Random Effects Models ◽  
Hazard Ratios ◽  
Unruptured Intracranial Aneurysms

Abstract Background Some studies have shown a protective association between aspirin use and subarachnoid haemorrhage (SAH). Other studies have found no relationship or the reverse. These studies differ in their study populations and definitions of SAH. Aims Our aim was to establish 1) if there is an association between aspirin and SAH, 2) how this differs between the general population and those with intracranial aneurysms. Summary of review Studies reporting aspirin use and the occurrence of SAH were included and grouped based on population (general population vs aneurysm population). Odds ratios, hazard ratios and confidence intervals were combined in random-effects models. 11 studies were included. Overall, there was an association between aspirin and SAH (OR 0.68 [0.48, 0.96]). However, populations were diverse and heterogeneity between studies high (p<0.00001), questioning the validity of combining these studies and justifying analysis by population. In the general population there was no difference in aspirin use between individuals with and without SAH (OR 1.15 [0.96, 1.38]). In patients with intracranial aneurysms, aspirin use was greater in patients without SAH (OR 0.37 [0.24, 0.58]), although these studies were at higher risk of bias. Conclusions There is an association between aspirin use and SAH in patients with intracranial aneurysms. This apparent protective relationship is not seen in the general population. Prospective randomised studies are required to further investigate the effect of aspirin on unruptured intracranial aneurysms.

Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

2012 ◽  
Vol 9 (5) ◽  
pp. 610-620 ◽  
Author(s):  
Thomas A Trikalinos ◽  
Ingram Olkin
Keyword(s):  
Random Effects ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Multiple Time ◽  
Time Points ◽  
Fixed And Random Effects ◽  
Multiple Time Points ◽  
Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

scholarly journals Metan: Fixed- and Random-Effects Meta-Analysis

2008 ◽  
Vol 8 (1) ◽  
pp. 3-28 ◽  
Author(s):  
Ross J. Harris ◽  
Jonathan J. Deeks ◽  
Douglas G. Altman ◽  
Michael J. Bradburn ◽  
Roger M. Harbord ◽  
...  
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Fixed And Random Effects

scholarly journals Multiple moderator meta-analysis using the R-package Meta-CART

2020 ◽  
Vol 52 (6) ◽  
pp. 2657-2673
Author(s):  
Xinru Li ◽  
Elise Dusseldorp ◽  
Xiaogang Su ◽  
Jacqueline J. Meulman
Keyword(s):  
Random Effects ◽  
Treatment Effectiveness ◽  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Tree Model ◽  
Effective Interventions ◽  
Look Ahead ◽  
Fixed And Random Effects ◽  
User Friendly

AbstractIn meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other’s effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-package metacart, which provides user-friendly functions to conduct meta-CART analyses in R. This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples.

Comparative Effectiveness of Lenalidomide Plus Dexamethasone for the Treatment of Refractory/Relapsed Multiple Myeloma: A Systematic Review and Mixed Treatment Comparison

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 4076-4076
Author(s):  
Sallie Stradwick ◽  
Nick Freemantle ◽  
John Snowden ◽  
Felipe Rodrigues ◽  
Nic Brereton
Keyword(s):  
Multiple Myeloma ◽  
Combination Therapy ◽  
Confidence Intervals ◽  
Random Effects ◽  
Comparative Effectiveness ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Treatment Comparison ◽  
Mixed Treatment ◽  
Secondary Outcomes

Abstract Abstract 4076 OBJECTIVES: To evaluate the comparative effectiveness of lenalidomide (25 mg) plus dexamethasone (40 mg) (LEN/dex) for the treatment of relapsed/refractory multiple myeloma (RRMM) compared to thalidomide and bendamustine. Primary outcome of interest was time to progression (TTP). Secondary outcomes of interest were overall response rates (ORR) and overall survival (OS). METHODS: A comprehensive systematic literature review was conducted to identify any randomised controlled trials (RCTs) investigating the clinical efficacy of specified therapies for the treatment of RRMM. Specified therapies included lenalidomide, thalidomide and bendamustine. Of these therapies, only LEN/dex combination therapy is approved in RRMM but bendamustine and thalidomide (monotherapy or in combination with dexamethasone) have also shown activity in the treatment of myeloma. Electronic databases were searched from March 2002 to 2012 (language unrestricted. Randomized clinical trials were independently evaluated against predetermined criteria for inclusion and determined to be eligible for the meta-analysis prior to any outcome assessment. Fixed effects and random effects mixed-treatment comparisons (MTC) were carried out, adopting the methods described by Lu and Ades1. MTCs estimate the comparative effectiveness of multiple treatments using an evidence base of trials that individually do not compare all treatment options. Results for TTP were reported as hazard ratios (HR) and 95% confidence intervals and an associated probability of best treatment. Results for binary variables (ORR, OS) were reported as odds ratios (OR) and 95% confidence intervals. RESULTS: Sixteen original RCTs met the initial inclusion criteria; twelve (N = 3,590), nine (N = 3,350) and six (N = 2,295) of which were able to be connected to form a network of evidence which provided the heterogeneous trial base for MTC analysis of ORR, OS, and TTP comparison, respectively. Two trials directly investigated the efficacy of LEN/dex (N = 353) and three trials assessed the efficacy of thalidomide monotherapy (N = 785). No RCTs were identified that investigated the efficacy of bendamustine or thalidomide/dexamethasone combination therapy in RRMM. There were too few studies (investigating treatment arms of interest) relative to the number of trials in the network to formally estimate or test for heterogeneity. Therefore a fixed effects network meta-analysis was used instead of a random effects network meta-analysis. Comparability of trial populations was assessed and discussed in detail since these could not be adjusted for in the statistical model. Age, sex, baseline disease characteristics, time since diagnosis and medical history were similar between study populations. TTP analysis was statistically significant and favoured LEN/dex over thalidomide monotherapy: HR = 2.34 [1.31, 4.17]. The associated probability of LEN/dex being the best treatment within the evidence network is 97.9%. Secondary outcomes analysis were also statistically significant in favour of LEN/dex over thalidomide monotherapy (ORR: OR = 10.48 [4.75, 22.81]; OS: OR = 1.43 [1.12, 1.84]). CONCLUSIONS: Results demonstrated statistically significant superiority of lenalidomide plus dexamethasone therapy versus thalidomide monotherapy for the treatment of RRMM. No analyses were possible versus bendamustine or thalidomide/dexamethasone combination therapy due to the lack of RCTs investigating the efficacy of these therapeutic regimens in RRMM. Disclosures: Stradwick: Celgene International: Consultancy. Freemantle:BresMed: Consultancy. Off Label Use: Thalidomide monotherapy activity in the treatment of myeloma. Brereton:Celgene International: Consultancy.

scholarly journals Confidence intervals for the overall effect size in random-effects meta-analysis.

2008 ◽  
Vol 13 (1) ◽  
pp. 31-48 ◽  
Author(s):  
Julio Sánchez-Meca ◽  
Fulgencio Marín-Martínez
Keyword(s):  
Confidence Intervals ◽  
Random Effects ◽  
Effect Size ◽  
Meta Analysis
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