scholarly journals Study specific prediction intervals for random‐effects meta‐analysis: A tutorial

2021 ◽  
Author(s):  
Robbie C. M. Aert ◽  
Christopher H. Schmid ◽  
David Svensson ◽  
Dan Jackson
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Prediction Intervals ◽  
Specific Prediction

scholarly journals Putting Variation into Variance: Modeling Between-Study Heterogeneity in Meta-Analysis

2021 ◽  
Author(s):  
Donald Ray Williams ◽  
Josue E. Rodriguez ◽  
Paul - Christian Bürkner
Keyword(s):  
Random Effects ◽  
Variance Components ◽  
Meta Analysis ◽  
R Package ◽  
Predictive Distribution ◽  
Prediction Intervals ◽  
Future Research ◽  
Variance Modeling ◽  
Specific Effects ◽  
Study Heterogeneity

We shed much needed light upon a critical assumption that is oft-overlooked---or not considered at all---in random-effects meta-analysis.Namely, that between-study variance is constant across \emph{all} studies which implies they are from the \emph{same} population. Yet it is not hard to imagine a situation where there are several and not merely one population of studies, perhaps differing in their between-study variance (i.e., heteroskedasticity). The objective is to then make inference, given that there are variations in heterogeneity. There is an immediate problem, however, in that modeling heterogeneous variance components is not straightforward to do in a general way. To this end, we propose novel methodology, termed Bayesian location-scale meta-analysis, that can accommodate moderators for both the overall effect (location) and the between-study variance (scale). After introducing the model, we then extend heterogeneity statistics, prediction intervals, and hierarchical shrinkage, all of which customarily assume constant heterogeneity, to include variations therein. With these new tools in hand, we go to work demonstrating that quite literally \emph{everything} changes when between-study variance is not constant across studies. The changes were not small and easily passed the interocular trauma test---the importance hits right between the eyes. Such examples include (but are not limited to) inference on the overall effect, a compromised predictive distribution, and improper shrinkage of the study-specific effects. Further, we provide an illustrative example where heterogeneity was not considered a mere nuisance to show that modeling variance for its own sake can provide unique inferences, in this case into discrimination across nine countries. The discussion includes several ideas for future research. We have implemented the proposed methodology in the {\tt R} package \textbf{blsmeta}.

scholarly journals Prediction intervals for random-effects meta-analysis: A confidence distribution approach

2018 ◽  
Vol 28 (6) ◽  
pp. 1689-1702 ◽  
Author(s):  
Kengo Nagashima ◽  
Hisashi Noma ◽  
Toshi A Furukawa
Keyword(s):  
Random Effects ◽  
Prediction Interval ◽  
Meta Analysis ◽  
Prediction Intervals ◽  
Point Estimate ◽  
Random Effects Models ◽  
Confidence Distribution ◽  
Large Sample Approximation ◽  
Sample Approximation ◽  
Meta Analyses

Prediction intervals are commonly used in meta-analysis with random-effects models. One widely used method, the Higgins–Thompson–Spiegelhalter prediction interval, replaces the heterogeneity parameter with its point estimate, but its validity strongly depends on a large sample approximation. This is a weakness in meta-analyses with few studies. We propose an alternative based on bootstrap and show by simulations that its coverage is close to the nominal level, unlike the Higgins–Thompson–Spiegelhalter method and its extensions. The proposed method was applied in three meta-analyses.

USING PREDICTION INTERVALS FROM RANDOM-EFFECTS META-ANALYSES IN AN ECONOMIC MODEL

2014 ◽  
Vol 30 (1) ◽  
pp. 44-49 ◽  
Author(s):  
Conor Teljeur ◽  
Michelle O'Neill ◽  
Patrick Moran ◽  
Linda Murphy ◽  
Patricia Harrington ◽  
...  
Keyword(s):  
Random Effects ◽  
Treatment Effect ◽  
Meta Analysis ◽  
Clinical Effectiveness ◽  
Cost Utility ◽  
Prediction Intervals ◽  
Confidence Bounds ◽  
Robot Assisted ◽  
Meta Analyses ◽  
Robot Assisted Surgery

Objectives: When incorporating treatment effect estimates derived from a random-effect meta-analysis it is tempting to use the confidence bounds to determine the potential range of treatment effect. However, prediction intervals reflect the potential effect of a technology rather than the more narrowly defined average treatment effect. Using a case study of robot-assisted radical prostatectomy, this study investigates the impact on a cost-utility analysis of using clinical effectiveness derived from random-effects meta-analyses presented as confidence bounds and prediction intervals, respectively.Methods: To determine the cost-utility of robot-assisted prostatectomy, an economic model was developed. The clinical effectiveness of robot-assisted surgery compared with open and conventional laparoscopic surgery was estimated using meta-analysis of peer-reviewed publications. Assuming treatment effect would vary across studies due to both sampling variability and differences between surgical teams, random-effects meta-analysis was used to pool effect estimates.Results: Using the confidence bounds approach the mean and median ICER was €24,193 and €26,731/QALY (95%CI: €13,752 to €68,861/QALY), respectively. The prediction interval approach produced an equivalent mean and median ICER of €26,920 and €26,643/QALY (95%CI: -€135,244 to €239,166/QALY), respectively. Using prediction intervals, there is a probability of 0.042 that robot-assisted surgery will result in a net reduction in QALYs.Conclusions: Using prediction intervals rather than confidence bounds does not affect the point estimate of the treatment effect. In meta-analyses with significant heterogeneity, the use of prediction intervals will produce wider ranges of treatment effect, and hence result in greater uncertainty, but a better reflection of the effect of the technology.

scholarly journals Frequentist performances of Bayesian prediction intervals for random‐effects meta‐analysis

2020 ◽  
Author(s):  
Yuta Hamaguchi ◽  
Hisashi Noma ◽  
Kengo Nagashima ◽  
Tomohide Yamada ◽  
Toshi A. Furukawa
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Bayesian Prediction ◽  
Prediction Intervals

scholarly journals Dyslipidaemia and mortality in COVID-19 patients - a meta-analysis

QJM ◽  
2021 ◽  
Author(s):  
Marco Zuin ◽  
Gianluca Rigatelli ◽  
Claudio Bilato ◽  
Carlo Cervellati ◽  
Giovanni Zuliani ◽  
...  
Keyword(s):  
Random Effects ◽  
Mortality Risk ◽  
Meta Analysis ◽  
Random Effect ◽  
Final Analysis ◽  
Short Term ◽  
Random Effects Models ◽  
Pooled Prevalence ◽  
Statistical Heterogeneity ◽  
Trim And Fill

Abstract Objective The prevalence and prognostic implications of pre-existing dyslipidaemia in patients infected by the SARS-CoV-2 remain unclear. To perform a systematic review and meta-analysis of prevalence and mortality risk in COVID-19 patients with pre-existing dyslipidaemia. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE and Scopus to locate all the articles published up to January 31, 2021, reporting data on dyslipidaemia among COVID-19 survivors and non-survivors. The pooled prevalence of dyslipidaemia was calculated using a random effects model and presenting the related 95% confidence interval (CI), while the mortality risk was estimated using the Mantel-Haenszel random effects models with odds ratio (OR) and related 95% CI. Statistical heterogeneity was measured using the Higgins I2 statistic. Results Eighteen studies, enrolling 74.132 COVID-19 patients [mean age 70.6 years], met the inclusion criteria and were included in the final analysis. The pooled prevalence of dyslipidaemia was 17.5% of cases (95% CI: 12.3-24.3%, p < 0.0001), with high heterogeneity (I2=98.7%). Pre-existing dyslipidaemia was significantly associated with higher risk of short-term death (OR: 1.69, 95% CI: 1.19-2.41, p = 0.003), with high heterogeneity (I2=88.7%). Due to publication bias, according to the Trim-and-Fill method, the corrected random-effect ORs resulted 1.61, 95% CI 1.13-2.28, p < 0.0001 (one studies trimmed). Conclusions Dyslipidaemia represents a major comorbidity in about 18% of COVID-19 patients but it is associated with a 60% increase of short-term mortality risk.

scholarly journals Incidence of Herpes Zoster in HIV-Infected Patients Undergoing Antiretroviral Therapy: A Systematic Review and Meta-analysis

2021 ◽  
Vol 10 (11) ◽  
pp. 2300
Author(s):  
Han-Chang Ku ◽  
Yi-Tseng Tsai ◽  
Sriyani-Padmalatha Konara-Mudiyanselage ◽  
Yi-Lin Wu ◽  
Tsung Yu ◽  
...  
Keyword(s):  
Systematic Review ◽  
Herpes Zoster ◽  
Antiretroviral Therapy ◽  
Random Effects ◽  
Meta Analysis ◽  
People Living With Hiv ◽  
Funnel Plots ◽  
Sex With Men ◽  
Treat All ◽  
Living With Hiv

The incidence of herpes zoster (HZ) in patients infected with HIV is higher than that of the general population. However, the incidence of HZ in HIV patients receiving antiretroviral therapy (ART) remains unclear. This meta-analysis aimed to estimate the pooled incidence rate and risk factors for HZ in the post-ART era. We identified studies assessing the incidence of HZ in the post-ART era between 1 January 2000 and 28 February 2021, from four databases. Pooled risk ratios were calculated from 11 articles using a random-effects model. The heterogeneity of the included trials was evaluated by visually inspecting funnel plots, performing random-effects meta-regression and using I2 statistics. Of the 2111 studies screened, we identified 11 studies that were eligible for final inclusion in the systematic review and 8 studies that were eligible for a meta-analysis. The pooled incidence of HZ in the post-ART era (after the introduction of ART in 1997) was 2.30 (95% confidence interval (CI): 1.56–3.05) per 100 person years (PYs). The risks of incidence of HZ among people living with HIV included male sex (AOR: 4.35 (95% CI: 054–2.41)), men who have sex with men (AOR: 1.21 (95% CI: −0.76–1.13)), CD4 count < 200 cells/μL (AOR: 11.59 (95% CI: 0.53–4.38)) and not receiving ART (AOR: 2.89 (95% CI: −0.44–2.56)). The incidence of HZ is substantially lower among HIV infected patients receiving ART than those not receiving ART. Initiating ART immediately after diagnosis to treat all HIV-positive individuals is crucial to minimize the disease burden of HZ.

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.

scholarly journals Coenzyme Q10 supplementation for prophylaxis in adult patients with migraine—a meta-analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e039358
Author(s):  
Suhairul Sazali ◽  
Salziyan Badrin ◽  
Mohd Noor Norhayati ◽  
Nur Suhaila Idris
Keyword(s):  
Random Effects ◽  
Coenzyme Q10 ◽  
Migraine Headache ◽  
Meta Analysis ◽  
Adult Patients ◽  
Control Group ◽  
Cochrane Central Register ◽  
Statistical Heterogeneity ◽  
Coq10 Supplementation ◽  
The Impact

ObjectiveTo determine the effects of coenzyme Q10 (CoQ10) for reduction in the severity, frequency of migraine attacks and duration of headache in adult patients with migraine.DesignSystematic review and meta-analysis.Data sourcesCochrane Central Register of Controlled Trials, CENTRAL, MEDLINE, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Psychological Information Database (PsycINFO) from inception till December 2019.Study selectionAll randomised control trials comparing CoQ10 with placebo or used as an adjunct treatment included in this meta-analysis. Cross-over designs and controlled clinical trials were excluded.Data synthesisHeterogeneity at face value by comparing populations, settings, interventions and outcomes were measured and statistical heterogeneity was assessed by means of the I2 statistic. The treatment effect for dichotomous outcomes were using risk ratios and risk difference, and for continuous outcomes, mean differences (MDs) or standardised mean difference; both with 95% CIs were used. Subgroup analyses were carried out for dosage of CoQ10 and if CoQ10 combined with another supplementation. Sensitivity analysis was used to investigate the impact risk of bias for sequence generation and allocation concealment of included studies.ResultsSix studies with a total of 371 participants were included in the meta-analysis. There is no statistically significant reduction in severity of migraine headache with CoQ10 supplementation. CoQ10 supplementation reduced the duration of headache attacks compared with the control group (MD: −0.19; 95% CI: −0.27 to −0.11; random effects; I2 statistic=0%; p<0.00001). CoQ10 usage reduced the frequency of migraine headache compared with the control group (MD: −1.52; 95% CI: −2.40 to −0.65; random effects; I2 statistic=0%; p<0.001).ConclusionCoQ10 appears to have beneficial effects in reducing duration and frequency of migraine attack.PROSPERO registration numberCRD42019126127.

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