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 Interval estimation of the overall treatment effect in random-effects meta-analyses: Recommendations from a simulation study comparing frequentist, Bayesian, and bootstrap methods

2020 ◽  
Author(s):  
Frank Weber ◽  
Guido Knapp ◽  
Anne Glass ◽  
Günther Kundt ◽  
Katja Ickstadt
Keyword(s):  
Literature Review ◽  
Random Effects ◽  
Simulation Study ◽  
Treatment Effect ◽  
Profile Likelihood ◽  
Meta Analysis ◽  
Artery Bypass ◽  
Interval Length ◽  
Interval Estimators ◽  
Meta Analyses

There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study is still lacking. Thus, we conduct such a simulation study for continuous and binary outcomes, focusing on the medical field for application.Based on the literature review and some new theoretical considerations, a practicable number of interval estimators is selected for this comparison: the classical normal-approximation interval using the DerSimonian-Laird heterogeneity estimator, the HKSJ interval using either the Paule-Mandel or the Sidik-Jonkman heterogeneity estimator, the Skovgaard higher-order profile likelihood interval, a parametric bootstrap interval, and a Bayesian interval using different priors. We evaluate the performance measures (coverage and interval length) at specific points in the parameter space, i.e. not averaging over a prior distribution. In this sense, our study is conducted from a frequentist point of view.We confirm the main finding of the literature review, the general recommendation of the HKSJ method (here with the Sidik-Jonkman heterogeneity estimator). For meta-analyses including only 2 studies, the high length of the HKSJ interval limits its practical usage. In this case, the Bayesian interval using a weakly informative prior for the heterogeneity may help. Our recommendations are illustrated using a real-world meta-analysis dealing with the efficacy of an intramyocardial bone marrow stem cell transplantation during coronary artery bypass grafting.

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.

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.

Clinical outcomes of cancer patients with COVID-19: A systematic review and meta-analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18600-e18600
Author(s):  
Maryam Alasfour ◽  
Salman Alawadi ◽  
Malak AlMojel ◽  
Philippos Apolinario Costa ◽  
Priscila Barreto Coelho ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Cancer Patients ◽  
Clinical Outcomes ◽  
Meta Analysis ◽  
Small Sample ◽  
Prediction Intervals ◽  
Invasive Ventilation ◽  
Newcastle Ottawa Scale ◽  
Small Sample Sizes ◽  
Meta Analyses

e18600 Background: Patients with coronavirus disease 2019 (COVID-19) and cancer have worse clinical outcomes compared to those without cancer. Primary studies have examined this population, but most had small sample sizes and conflicting results. Prior meta-analyses exclude most US and European data or only examine mortality. The present meta-analysis evaluates the prevalence of several clinical outcomes in cancer patients with COVID-19, including new emerging data from Europe and the US. Methods: A systematic search of PubMED, medRxiv, JMIR and Embase by two independent investigators included peer-reviewed papers and preprints up to July 8, 2020. The primary outcome was mortality. Other outcomes were ICU and non-ICU admission, mild, moderate and severe complications, ARDS, invasive ventilation, stable, and clinically improved rates. Study quality was assessed through the Newcastle–Ottawa scale. Random effects model was used to derive prevalence rates, their 95% confidence intervals (CI) and 95% prediction intervals (PI). Results: Thirty-four studies (N = 4,371) were included in the analysis. The mortality prevalence rate was 25.2% (95% CI: 21.1–29.7; 95% PI: 9.8-51.1; I 2 = 85.4), with 11.9% ICU admissions (95% CI: 9.2-15.4; 95% PI: 4.3-28.9; I 2= 77.8) and 25.2% clinically stable (95% CI: 21.1-29.7; 95% PI: 9.8-51.1; I 2 = 85.4). Furthermore, 42.5% developed severe complications (95% CI: 30.4-55.7; 95% PI: 8.2-85.9; I 2 = 94.3), with 22.7% developing ARDS (95% CI: 15.4-32.2; 95% PI: 5.8-58.6; I 2 = 82.4), and 11.3% needing invasive ventilation (95% CI: 6.7-18.4; 95% PI: 2.3-41.1; I 2 = 79.8). Post-follow up, 49% clinically improved (95% CI: 35.6-62.6; 95% PI: 9.8-89.4; I 2 = 92.5). All outcomes had large I 2 , suggesting high levels of heterogeneity among studies, and wide PIs indicating high variability within outcomes. Despite this variability, the mortality rate in cancer patients with COVID-19, even at the lower end of the PI (9.8%), is higher than the 2% mortality rate of the non-cancer with COVID-19 population, but not as high as what other meta-analyses conclude, which is around 25%. Conclusions: Patients with cancer who develop COVID-19 have a higher probability of mortality compared to the general population with COVID-19, but possibly not as high as previous studies have shown. A large proportion of them developed severe complications, but a larger proportion recovered. Prevalence of mortality and other outcomes published in prior meta-analyses did not report prediction intervals, which compromises the clinical utilization of such results.

Alternative Tendon Coaptations to the Pulvertaft Weave Technique: A Systematic Review and Meta-Analysis of Biomechanical Studies

Hand ◽  
2021 ◽  
pp. 155894472110432
Author(s):  
Emily M. Graham ◽  
Jeremie D. Oliver ◽  
Russell Hendrycks ◽  
Dino Maglic ◽  
Shaun D. Mendenhall
Keyword(s):  
Systematic Review ◽  
Yield Strength ◽  
Random Effects ◽  
Meta Analysis ◽  
Dichotomous Data ◽  
Inverse Variance ◽  
Significant Difference ◽  
Alternative Techniques ◽  
Meta Analyses

Background The Pulvertaft weave technique (PT) is frequently used during tendon repairs and transfers. However, this technique is associated with limitations. In this systematic review and meta-analysis, quantitative and qualitative analyses were performed on in vitro, biomechanical studies that compared the PT with alternative techniques. Methods Articles included for qualitative and/or qualitative analysis were identified following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies included in the meta-analysis were analyzed either as continuous data with inverse variance and random effects or as dichotomous data using a Mantel-Haenszel analysis assuming random effects to calculate an odds ratio. Results A comprehensive electronic search yielded 8 studies meeting inclusion criteria for meta-analysis. Two studies with a total of 65 tendon coaptations demonstrated no significant difference in strength between the PT and traditional side-to-side (STS) techniques ( P = .92). Two studies with a total of 43 tendon coaptations showed that the STS with 1 weave has a higher yield strength than the PT ( P = .03). Two studies with a total of 62 tendon repairs demonstrated no significant difference in strength between the PT and the step-cut (SC) techniques ( P = .70). The final 2 studies included 46 tendon repairs and demonstrated that the wrap around (WA) technique has a higher yield strength than the PT ( P < .001). Conclusions The STS, SC, and WA techniques are preferred for improving tendon form. The STS and WA techniques have superior yield strengths than the PT, and the SC technique withstands similar stress to failure as the PT.

Meta-Analysis of Meta-Analyses in Communication: Comparing Fixed Effects and Random Effects Analysis Models

2010 ◽  
Vol 58 (3) ◽  
pp. 257-278 ◽  
Author(s):  
Ashley Anker ◽  
Amber Marie Reinhart ◽  
Thomas Hugh Feeley
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Meta Analyses ◽  
Analysis Models

Systematic Review and Meta-Analysis of Classroom-Wide Social–Emotional Interventions for Preschool Children

2020 ◽  
pp. 027112142093557 ◽  
Author(s):  
Li Luo ◽  
Brian Reichow ◽  
Patricia Snyder ◽  
Jennifer Harrington ◽  
Joy Polignano
Keyword(s):  
Systematic Review ◽  
Preschool Children ◽  
Social Competence ◽  
Random Effects ◽  
Challenging Behavior ◽  
Emotional Competence ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Social Emotional ◽  
Meta Analyses

Background: All children benefit from intentional interactions and instruction to become socially and emotionally competent. Over the past 30 years, evidence-based intervention tactics and strategies have been integrated to establish comprehensive, multitiered, or hierarchical systems of support frameworks to guide social–emotional interventions for young children. Objectives: To review systematically the efficacy of classroom-wide social–emotional interventions for improving the social, emotional, and behavioral outcomes of preschool children and to use meta-analytic techniques to identify critical study characteristics associated with obtained effect sizes. Method: Four electronic databases (i.e., Academic Search Premier, Educational Resource Information Center, PsycINFO, and Education Full Text) were systematically searched in December 2015 and updated in January 2018. “Snowball methods” were used to locate additional relevant studies. Effect size estimates were pooled using random-effects meta-analyses for three child outcomes, and moderator analyses were conducted. Results: Thirty-nine studies involving 10,646 child participants met the inclusion criteria and were included in this systematic review, with 33 studies included in the meta-analyses. Random-effects meta-analyses showed improvements in social competence ( g = 0.42, 95% confidence interval [CI] = [0.28, 0.56]) and emotional competence ( g = 0.33, 95% CI = [0.10, 0.56]), and decreases in challenging behavior ( g = −0.31, 95% CI = [−0.43, −0.19]). For social competence and challenging behavior, moderator analyses suggested interventions with a family component had statistically significant and larger effect sizes than those without a family component. Studies in which classroom teachers served as the intervention agent produced statistically significant but smaller effect sizes than when researchers or others implemented the intervention for challenging behavior. Conclusion: This systematic review and meta-analysis support using comprehensive social–emotional interventions for all children in a preschool classroom to improve their social–emotional competence and reduce challenging behavior.

scholarly journals Effectiveness of Electronic Cigarettes in Smoking Cessation: A Systematic Review and Meta-analysis

2020 ◽  
Author(s):  
Igor Grabovac ◽  
Moritz Oberndorfer ◽  
Jismy Fischer ◽  
Winfried Wiesinger ◽  
Sandra Haider ◽  
...  
Keyword(s):  
Systematic Review ◽  
Smoking Cessation ◽  
Random Effects ◽  
Electronic Cigarettes ◽  
Meta Analysis ◽  
Current Evidence ◽  
Quality Of Evidence ◽  
Registration Number ◽  
Meta Analyses

Abstract Introduction Reports of the effectiveness of e-cigarettes (ECs) for smoking cessation vary across different studies making implementation recommendations hard to attain. We performed a systematic review and meta-analysis to assess the current evidence regarding effectiveness of ECs for smoking cessation. Methods PubMed, PsycInfo, and Embase databases were searched for randomized controlled trials comparing nicotine ECs with non-nicotine ECs or with established smoking cessation interventions (nicotine replacement therapy [NRT] and or counseling) published between 1 January 2014 and 27 June 2020. Data from eligible studies were extracted and used for random-effects meta-analyses (PROSPERO registration number: CRD42019141414). Results The search yielded 13 950 publications with 12 studies being identified as eligible for systematic review (N = 8362) and 9 studies for random-effects meta-analyses (range: 30–6006 participants). The proportion of smokers achieving abstinence was 1.71 (95 CI: 1.02–2.84) times higher in nicotine EC users compared with non-nicotine EC users. The proportion of abstinent smokers was 1.69 (95 CI: 1.25–2.27) times higher in EC users compared with participants receiving NRT. EC users showed a 2.04 (95 CI: 0.90–4.64) times higher proportion of abstinent smokers in comparison with participants solely receiving counseling. Conclusions Our results suggest that nicotine ECs may be more effective in smoking cessation when compared with placebo ECs or NRT. When compared with counseling alone, nicotine ECs are more effective short term, but its effectiveness appears to diminish with later follow-ups. Given the small number of studies, heterogeneous design, and the overall moderate to low quality of evidence, it is not possible to offer clear recommendations. Implications The results of this study do not allow for a conclusive argument. However, pooling current evidence points toward a potential for ECs as a smoking cessation tool. Though, given the overall quality of evidence, future studies should aim for more clarity in terms of interventions and larger study populations.

Systematic Review of Second Primary Oropharyngeal Cancers in Patients With p16+ Oropharyngeal Cancer

2020 ◽  
pp. 019459982095117
Author(s):  
Craig A. Bollig ◽  
David S. Lee ◽  
Angela L. Mazul ◽  
Katelyn Stepan ◽  
Sidharth V. Puram ◽  
...  
Keyword(s):  
Surgical Treatment ◽  
Random Effects ◽  
Meta Analysis ◽  
Second Primary ◽  
Random Effects Model ◽  
Treatment Information ◽  
Pooled Prevalence ◽  
Alive With Disease ◽  
Medical Librarian ◽  
Meta Analyses

Objective To systematically review the literature to determine the prevalence and clinical outcomes of second primary oropharyngeal squamous cell carcinoma (OPSCC). Data Sources Search strategies created with a medical librarian were implemented using multiple databases in October 2019. Review Methods The population of interest included adults age >18 years with a p16+ or human papillomavirus-positive OPSCC. The outcome was a synchronous or metachronous second primary OPSCC. Inclusion and exclusion criteria were designed to capture all study designs. In total, 685 records were identified by the search strategy. Two reviewers independently performed the review, extracted data, and performed a quality assessment. Primary Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A random-effects model was used for the meta-analysis. Results A total of 2470 patients with 35 second primary OPSCCs from 15 studies were identified. The pooled prevalence of second primary OPSCC was 1.4% (range, 0%-14.3%). In the random-effects model, the prevalence was estimated at 1.3% (95% CI, 0.7%-2.3%; P = .51, I2 = 52%). Of the 30 patients with treatment information, 26 (86.7%) received surgical treatment, while 4 (13.3%) underwent nonsurgical therapy. Of the 29 patients with available survival information, 22 (75.9%) had no evidence of disease at last follow-up, 5 (17.2%) ultimately died of disease, and 2 (6.9%) were alive with disease. Conclusion Overall, the rate of second primary OPSCC in patients with an index p16+ OPSCC is low, and most patients are successfully treated. Insufficient evidence currently exists to recommend routine elective tonsillectomy during surgical treatment of p16+ OPSCC.

Bivariate Random-effects Meta-analysis of Sensitivity and Specificity with SAS PROC GLIMMIX

2010 ◽  
Vol 49 (01) ◽  
pp. 54-64 ◽  
Author(s):  
J. Menke
Keyword(s):  
Sensitivity And Specificity ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Meta Analysis ◽  
Empirical Evaluation ◽  
Test Accuracy ◽  
Random Effects Model ◽  
Operating Characteristics ◽  
Meta Analyses

Summary Objectives: Meta-analysis allows to summarize pooled sensitivities and specificities from several primary diagnostic test accuracy studies. Often these pooled estimates are indirectly obtained from a hierarchical summary receiver operating characteristics (HSROC) analysis. This article presents a generalized linear random-effects model with the new SAS PROC GLIMMIX that obtains the pooled estimates for sensitivity and specificity directly. Methods: Firstly, the formula of the bivariate random-effects model is presented in context with the literature. Then its implementation with the new SAS PROC GLIMMIX is empirically evaluated in comparison to the indirect HSROC approach, utilizing the published 2 x 2 count data of 50 meta-analyses. Results: According to the empirical evaluation the meta-analytic results from the bivariate GLIMMIX approach are nearly identical to the results from the indirect HSROC approach. Conclusions: A generalized linear mixed model with PROC GLIMMIX offers a straightforward method for bivariate random-effects meta-analysis of sensitivity and specificity.

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