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 Comparison of Pain Perception between Clear Aligners and Fixed Appliances: A Systematic Review and Meta-Analysis

2020 ◽  
Vol 10 (12) ◽  
pp. 4276 ◽  
Author(s):  
Dinis Pereira ◽  
Vanessa Machado ◽  
João Botelho ◽  
Luís Proença ◽  
José João Mendes ◽  
...  
Keyword(s):  
Random Effects ◽  
Pain Perception ◽  
Randomized Clinical Trials ◽  
Meta Analysis ◽  
Multiple Time ◽  
Case Control Studies ◽  
Newcastle Ottawa Scale ◽  
Fixed Appliances ◽  
Multiple Time Points ◽  
Meta Analyses

We aimed to compare the pain discomfort levels between clear aligners and fixed appliances at multiple time points. Four electronic databases (Pubmed, Medline, CENTRAL and Scholar) were searched up to May 2020. There were no year or language restrictions. Randomized clinical trials and case–control studies comparing pain perception through pain visual analog scale (VAS) in patients treated with clear aligners and with fixed appliances were included. Risk of bias within and across studies was assessed using Cochrane tool and Newcastle–Ottawa Scale (NOS) approach. Random-effects meta-analysis were conducted. VAS score and analgesic consumption were collected. Random-effects meta-analyses were used to synthesize available data. Following the review protocol, five articles met the inclusion criteria and were included, with a total of 273 participants (177 females, 96 males). Overall, clear aligners were associated with significantly less pain than fixed appliances during the first seven days of orthodontic treatment. Patients treated with clear aligners experience less pain discomfort than those treated with fixed appliances and consume less analgesics, with SORT A recommendation.

scholarly journals Meta-Analysis of Effect Sizes Reported at Multiple Time Points Using General Linear Mixed Model

PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0164898 ◽  
Author(s):  
Alfred Musekiwa ◽  
Samuel O. M. Manda ◽  
Henry G. Mwambi ◽  
Ding-Geng Chen
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Meta Analysis ◽  
Effect Sizes ◽  
General Linear ◽  
Multiple Time ◽  
Time Points ◽  
Multiple Time Points ◽  
General Linear Mixed Model

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 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.

scholarly journals Temporal variation in lymphocyte proteomics

2021 ◽  
Author(s):  
Michaela A McCown ◽  
Carolyn Allen ◽  
Daniel D Machado ◽  
Hannah Boekweg ◽  
Yiran Liang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Small Sample ◽  
Lymphocytic Leukemia ◽  
Small Samples ◽  
Tumor Evolution ◽  
Multiple Time ◽  
Time Points ◽  
Asymptomatic Stage ◽  
Multiple Time Points ◽  
Over Time

Chronic Lymphocytic Leukemia (CLL) is a slow progressing disease, characterized by a long asymptomatic stage followed by a symptomatic stage during which patients receive treatment. While proteomic studies have discovered differential pathways in CLL, the proteomic evolution of CLL during the asymptomatic stage has not been studied. In this pilot study, we show that by using small sample sizes comprising ~145 cells, we can detect important features of CLL necessary for studying tumor evolution. Our small samples are collected at two time points and reveal large proteomic changes in healthy individuals over time. A meta-analysis of two CLL proteomic papers showed little commonality in differentially expressed proteins and demonstrates the need for larger control populations sampled over time. To account for proteomic variability between time points and individuals, large control populations sampled at multiple time points are necessary for understanding CLL progression. Data is available via ProteomeXchange with identifier PXD027429.

scholarly journals Accuracy and Precision of Fixed and Random Effects in Meta-Analyses of Randomized Control Trials for Continuous Outcomes

2022 ◽  
Author(s):  
Timo Gnambs ◽  
Ulrich Schroeders
Keyword(s):  
Random Effects ◽  
Ad Hoc ◽  
Variance Estimation ◽  
Effect Sizes ◽  
Randomized Control Trials ◽  
Fixed And Random Effects ◽  
Regression Approach ◽  
Randomized Control ◽  
Meta Regression ◽  
Meta Analyses

Meta-analyses of treatment effects in randomized control trials are often faced with the problem of missing information required to calculate effect sizes and their sampling variances. Particularly, correlations between pre- and posttest scores are frequently not available. As an ad-hoc solution, researchers impute a constant value for the missing correlation. As an alternative, we propose adopting a multivariate meta-regression approach that models independent group effect sizes and accounts for the dependency structure using robust variance estimation or three-level modeling. A comprehensive simulation study mimicking realistic conditions of meta-analyses in clinical and educational psychology suggested that the prevalent imputation approach works well for estimating the pooled effect but severely distorts the between-study heterogeneity. In contrast, the robust meta-regression approach resulted in largely unbiased fixed and random effects. Based on these results recommendations for meta-analytic practice and future meta-analytic developments are provided.

scholarly journals Fatigue symptoms associated with COVID-19 in convalescent or recovered COVID-19 patients; a systematic review and meta-analysis

2021 ◽  
Author(s):  
Sanjay Rao ◽  
Tarek Benzouak ◽  
Sasha Gunpat ◽  
Rachel Burns ◽  
Tayyeb A. Tahir ◽  
...  
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Female Gender ◽  
Acute Stage ◽  
Self Report ◽  
Multiple Time ◽  
Cochrane Central Register ◽  
Central Register ◽  
Multiple Time Points ◽  
Meta Analyses

BackgroundThe prevalence and prognosis of post-acute stage SARS-CoV-2 infection fatigue symptoms remain largely unknown.AimsWe performed a systematic review to evaluate the prevalence of fatigue in post-recovery from SARS-CoV-2 infection.MethodMedline, Embase, PsycINFO, CINAHL, Web of Science, Scopus, trial registries, Cochrane Central Register of Controlled Trials and Google Scholar were searched for studies on fatigue in samples that recovered from PCR diagnosed COVID-19. Meta-analyses were conducted separately for each recruitment setting.ResultsWe identified 39 studies with 8825 patients that recovered from COVID-19. Post-COVID-19 patients self-report of fatigue was higher compared to healthy controls (RR = 3.688, 95%CI [2.502, 5.436], p < 0.001). Over 50% of patients discharged from inpatient care reported symptoms of fatigue during the first (ER = 0.517, 95%CI [0.278, 0.749]) and second month following recovery (ER = 0.527, 95%CI [0.337, 0.709]). 10% of the community patients reported fatigue in the first month post-recovery. Patient setting moderated the association between COVID-19 recovery and fatigue symptoms (R2 = 0.12, p < 0.001). Female gender was associated with greater self-report of fatigue (OR =1.782, 95%CI [1.531, 2.870]). Patients recruited through social media had fatigue above 90% across multiple time points. Fatigue was highest in studies from Europe.ConclusionFatigue is a symptom associated with functional challenges which could have economic and social impacts. Developing long-term planning for fatigue management amongst patients beyond acute stages of SARS-CoV-2 infection is essential to optimizing patient care and public health outcomes.

scholarly journals Fatigue symptoms associated with COVID-19 in convalescent or recovered COVID-19 patients; a systematic review and meta-analysis

2021 ◽  
Author(s):  
Sanjay Rao ◽  
Tarek Benzouak ◽  
Sasha Gunpat ◽  
Rachel J. Burns ◽  
Tayyeb A. Tahir ◽  
...  
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Female Gender ◽  
Acute Stage ◽  
Self Report ◽  
Multiple Time ◽  
Cochrane Central Register ◽  
Central Register ◽  
Multiple Time Points ◽  
Meta Analyses

BackgroundThe prevalence and prognosis of post-acute stage SARS-CoV-2 infection fatigue symptoms remain largely unknown.AimsWe performed a systematic review to evaluate the prevalence of fatigue in post-recovery from SARS-CoV-2 infection.MethodMedline, Embase, PsycINFO, CINAHL, Web of Science, Scopus, trial registries, Cochrane Central Register of Controlled Trials and Google Scholar were searched for studies on fatigue in samples that recovered from PCR diagnosed COVID-19. Meta-analyses were conducted separately for each recruitment setting.ResultsWe identified 39 studies with 8825 patients that recovered from COVID-19. Post-COVID-19 patients self-report of fatigue was higher compared to healthy controls (RR = 3.688, 95%CI [2.502, 5.436], p < 0.001). Over 50% of patients discharged from inpatient care reported symptoms of fatigue during the first (ER = 0.517, 95%CI [0.278, 0.749]) and second month following recovery (ER = 0.527, 95%CI [0.337, 0.709]). 10% of the community patients reported fatigue in the first month post-recovery. Patient setting moderated the association between COVID-19 recovery and fatigue symptoms (R2 = 0.12, p < 0.001). Female gender was associated with greater self-report of fatigue (OR =1.782, 95%CI [1.531, 2.870]). Patients recruited through social media had fatigue above 90% across multiple time points. Fatigue was highest in studies from Europe.ConclusionFatigue is a symptom associated with functional challenges which could have economic and social impacts. Developing long-term planning for fatigue management amongst patients beyond acute stages of SARS-CoV-2 infection is essential to optimizing patient care and public health outcomes.

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