# Introduction to Meta Regression

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## Meta Regression sentence examples within Effect Meta Regression

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Random-effects meta regression estimated whether sex differences in not enrolling ("screen out") varied by various individual, trial or site characteristics.

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Finally, the temporal trend in TV effects was evaluated by a random-effect meta regression model after obtaining the prefecture-year-specific effects.

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## Meta Regression sentence examples within meta regression analysi

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A meta regression analysis was carried out primarily on the pooled peri-implant bone level changes followed by implant loss and mid-buccal mucosa level change.

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Meta regression analysis showed that the DKD model induced by different methods (type I/II), the dose of Tripterygium wilfordii and the intervention time were not the reasons for the heterogeneity of 24 h-UP, Alb, Glu, Scr, and BUN (p > 0.

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## Meta Regression sentence examples within meta regression analysis

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A fixed effect sensitivity test was performed for the primary outcome, in addition to subgroup and meta regression analyses with predefined criteria.

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## Meta Regression sentence examples within meta regression revealed

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Meta regression revealed that the cutoff value and status of serum Tg were sources of heterogeneity for sensitivity, and the cutoff value was source of heterogeneity for specificity.

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Moreover, univariate meta regression revealed that the number of pulses per session could impose negative moderation toward the intervention.

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## Meta Regression sentence examples within meta regression model

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For acquisition, we train a meta regression model to estimate the segment-wise Intersection over Union (IoU) of each predicted segment of unlabeled images.

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Finally, the temporal trend in TV effects was evaluated by a random-effect meta regression model after obtaining the prefecture-year-specific effects.

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Meta regression analyses were used to evaluate the influence of sex.

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We explored quantitative heterogeneity by subgroup analyses, meta regression and evaluating the I2 statistic.

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A meta regression analysis was carried out primarily on the pooled peri-implant bone level changes followed by implant loss and mid-buccal mucosa level change.

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Meta regression statistical tests were performed in order to identify differences in those outcome parameters.

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Sub-group analysis and meta regression were performed to identify the sources of heterogeneity.

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Meta regression was used to explore the heterogeneity.

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A fixed effect sensitivity test was performed for the primary outcome, in addition to subgroup and meta regression analyses with predefined criteria.

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Data analysis: Weighted mean effect summaries using random-effects models for EP and CT, as well as meta regressions with robust standard errors to explore confounders.

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Meta regression analysis showed that the DKD model induced by different methods (type I/II), the dose of Tripterygium wilfordii and the intervention time were not the reasons for the heterogeneity of 24 h-UP, Alb, Glu, Scr, and BUN (p > 0.

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When the heterogeneity is large (I2 ≥ 50%), meta regression will be used to explore the source of inter-study heterogeneity.

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Based on Meta regression, with the increase in the sample size and the year of the research, prevalence of sleep disorder in the older adults grows, which is statistically significant (p < 0.

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Meta regression showed that the factors affecting the latent infection rate of HEVs in Chinese healthy population included sampling period, sampling area, and study population.

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In order to identification of potential sources of heterogeneity, predefined subgroup and meta regression analyses was conducted.

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To assess the heterogeneity, Q-test,
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statistics, and Meta regression and to search for the publication bias, Eggers's test and funnel plot were used.

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This paper briefly aims to introduce the meta regression application or better known as the Meta Regression Analysis (MRA).

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Methods We performed meta-analysis and meta regression to analyze the associations of plasma D-dimer with 106 clinical variables to identify a panoramic view of the derangements of fibrinolysis in 14,862 patients of 42 studies.

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Methods: This paper takes the correlation coefficient between strategic change and organizational performance as the effect value, and conducts Meta integration analysis and Meta regression analysis on 23 important literatures involving 7225 enterprise samples from 2008 to 2018.

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Meta regression analyses found narrower confidence intervals for interventions carried out over 3 months and tailored to the precise needs of class-level teachers for personal accomplishment.

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For acquisition, we train a meta regression model to estimate the segment-wise Intersection over Union (IoU) of each predicted segment of unlabeled images.

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The meta-analysis and Meta regressions were conducted using STATA 14 software.

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Random effect meta-analysis was used to conduct analysis and
the Cochran test and meta regression were also performed by STATA (ver.

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We used ordinary least squares regression and dominance analysis to judge the influence of socioeconomic and health behavior factors on life expectancy, and the final contribution of influencing factors to independent variables was generalized by Meta regression.

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To verify the moderation effect, a meta regression analysis and meta ANOVA were performed.

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Meta regression analysis was carried out to identify potential sources of heterogeneity.

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Meta regression analysis was done to identify probable sources of heterogeneity.

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Sensitivity analysis and meta regression were used to determine heterogeneity and test robustness.

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Citation: Chow R, Lock M, Lee SL, Lo SS and Simone CB II (2021) Esophageal Cancer Radiotherapy Dose Escalation Meta Regression Commentary: “High vs.

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5$) which we term meta classification, and we predict $IoU$ values directly which we term meta regression.

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For risk factors, dichotomous variables were analyzed with generalized linear model while a conventional meta regression with logit transformation was conducted for continuous variables.

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Meta regression analysis is an analysis that can summarize the results of research with the same topic so that a
conclusion is obtained in the form of effect size and can explain the heterogeneity of the results of several studies.

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To investigate these variations, the present study synthesizes the existing findings and conducting meta regression analysis on a broad level.

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A systematic literature review and meta regression was performed comparing Colombian vs LATAM patients.

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3) software will be used for data synthesis, sensitivity analysis, meta regression, subgroup analysis and risk of bias assessment.

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Meta regression show that sex differences in SBP were consistent with increasing age, stroke severity, other comorbidities and medication history.

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Moment of methods meta regression analysis was conducted between GDMT and all-cause mortality and cardiovascular outcomes.

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Subgroup analyses, meta regression, sensitivity analysis and publication bias were further performed.

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Meta regression was performed to explore the sources of heterogeneity.

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Random-effects meta regression estimated whether sex differences in not enrolling ("screen out") varied by various individual, trial or site characteristics.

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Meta regression was performed to detect the sources of heterogeneity and moderator variables on the study effect.

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OBJECTIVE
A systematic review, meta-analysis and meta regression of observational studies assessing the association between post stroke depression and risk of stroke recurrence and mortality.

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Meta regression showed that sample size was the main source of heterogeneity.

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We analysed how the estimate of disparity is associated with the estimation uncertainty using rank correlation and linear/quantile/panel/meta regression; this multiplicity of approaches was used to check the robustness of the results against confounding, skewness, hierarchical data, and possible changes over time.

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We carried out a meta regression according to the type of evaluated service, institution and methodological quality.

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Meta regression showed that age and gender were not related to the effect of sulfonylurea on fracture.

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Meta regression revealed that the cutoff value and status of serum Tg were sources of heterogeneity for sensitivity, and the cutoff value was source of heterogeneity for specificity.

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Interstudy heterogeneity will be assessed using the I2 statistic and explored through meta regressions and subgroup analyses.

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Moreover, univariate meta regression revealed that the number of pulses per session could impose negative moderation toward the intervention.

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Subgroup analysis, meta regression and sensitivity analysis were used to identify the potential sources of heterogeneity.

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The subgroup and Meta regression analysis were conducted by country sample size and year of publication.

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Random effects meta-analysis was employed followed by univariable and multivariable meta regression.

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These are then aggregated over predicted segments for either classifying between IoU=0 and IoU>0 (meta classification) or predicting the IoU via linear regression (meta regression).

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Heterogeneity may be explored by conducting subgroup analyses or meta regression.

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Moderator, sensitivity, and meta regression analyses were conducted to explore causes of heterogeneity and impact of study quality.

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Subgroup analysis and meta regression was performed to identify the sources of heterogeneity.

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In meta regression, higher body mass index was associated with greater improvements in overall EFs performance (β=0.

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Finally, a meta regression were noted in which the percentage restriction of daily energy intake inversely correlated with plasma IGF-1 levels (p = 0.

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Meta regression was used to explore if city-specific characteristics modified the air pollution-LCM association.

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A random-effects model was used and subgroup analysis and meta regression were performed to identify possible sources of heterogeneity.

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Considering the literature with meta regression analysis suggests (a) no technology intervention routinely results in improved learning outcomes across studies; this is despite evidence of (b) publication bias that favors papers with statistically significant results.

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Finally, the temporal trend in TV effects was evaluated by a random-effect meta regression model after obtaining the prefecture-year-specific effects.

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The relative increase in the daily costs was estimated using random effects meta regression.

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