## What is/are Meta Regression Analyses?

Meta Regression Analyses - Then, subgroup and meta-regression analyses were conducted to disclose the influence of geographic area, fracture type, administration route, frequency and dosage of TXA, blood transfusion threshold, and follow-up duration on the overall effect.^{[1]}Univariate meta-regression analyses found that sham response was associated with higher risk of blinding bias, and with treatment effect size in the active tDCS group.

^{[2]}Sensitivity and meta-regression analyses were performed.

^{[3]}Meta-regression analyses partially accounted for the source of heterogeneity, and yet, 53% of the symptoms remained unexplained.

^{[4]}Additional meta-regression analyses indicated that participant age and clinical status (i.

^{[5]}The meta-analysis and meta-regression analyses were fit using a random effects model.

^{[6]}To explore heterogeneity across the studies, univariable and multivariable meta-regression analyses were performed.

^{[7]}Meta-regression analyses, publication bias assessment and Trim and Fill function were also performed.

^{[8]}In meta-regression analyses, the treatment response was associated with patient age (estimated slope, -1.

^{[9]}Sub-group, sensitivity and meta-regression analyses were performed where data permitted.

^{[10]}On the other hand, meta-regression analyses performed on gender, age, duration of disease, percentage of patients with ANA+ or RF+, medium value of ESR or CRP were not statistically significant.

^{[11]}After duplicate study selection, data extraction, and risk-of-bias assessment, random effects meta-analyses of mean differences (MDs) and their 95% confidence intervals (CIs) were performed, followed by subgroup/meta-regression analyses.

^{[12]}The pooled sensitivity, specificity, and summary area under the receiver operating characteristic curve (AUC) were calculated and meta-regression analyses were performed.

^{[13]}The meta-regression analyses did not indicate the dose effects of PCs on SBP, DBP, PP and MAP.

^{[14]}We performed subgroup, sensitivity, and meta-regression analyses.

^{[15]}Subgroup and meta-regression analyses were conducted for demographic and clinical variables as appropriate.

^{[16]}Conducting meta-regression analyses, however, we found the relationship between ELM and resting vagal activity to significantly vary as a function of both age and presence of psychopathology.

^{[17]}Meta-regression analyses did not show a significant influence of RT nor HT on the DFI at 10 years.

^{[18]}A growing number of meta-analyses supported the application of digital psychotherapeutic intervention across different populations, but relatively few meta- and meta-regression analyses have concentrated on perinatal women.

^{[19]}Sensitivity analyses will be performed as well as a priori subgroup, meta-regression and multiple meta-regression analyses.

^{[20]}Pooled mean differences were calculated using a random-effects model, followed by sensitivity and meta-regression analyses.

^{[21]}In subgroup meta-regression analyses, males with definite fertility had continuous declines in SC (slope northern group=-2.

^{[22]}A meta-regression analyses determined that intralesional-GTR improved PFS (PHR 0.

^{[23]}Following the meta-analysis of random effects, the meta-regression analyses were used to explore factors potentially influencing treatment efficacy.

^{[24]}Meta-regression analyses showed a better hypoglycaemia detection in studies indicating a higher overall accuracy, whereas year of publication did not significantly influence diagnostic accuracy.

^{[25]}Network meta-analysis was performed to determine the effects of coffee and/or tea consumption on reducing BC risk in a dose-dependent manner and differences in coffee/tea type, menopause status, hormone receptor and the BMI in subgroup and meta-regression analyses.

^{[26]}Data were analyzed using random-effects modeling, and subgroup and meta-regression analyses were used to ascertain heterogeneity among the subgroups.

^{[27]}Meta-regression analyses also showed significant differences in lesion-level prevalence with respect to age (p = 0.

^{[28]}The relationship between each biomarker and WMH burden will be meta-analyzed if possible, with subgroup or meta-regression analyses to assess differences between diseases.

^{[29]}Data were synthesised with random-effects meta-analyses and meta-regression analyses, applying Grades of Recommendation, Assessment, Development and Evaluation criteria.

^{[30]}Subgroup analyses were crude univariable meta-regression analyses.

^{[31]}Temporal trends in clinical outcomes (cardiac death, myocardial infarction [MI], target lesion revascularisation [TLR], stent thrombosis [ST]) were assessed using random-effects meta-regression analyses, estimating the relationship between clinical outcomes and study start year.

^{[32]}Sources of heterogeneity were not found through subgroup and meta-regression analyses.

^{[33]}For the moderate response rate, meta-regression analyses revealed that publication year (β = −0.

^{[34]}Meta-regression analyses were performed to explore heterogeneity.

^{[35]}Following quality assessment of study eligibility, stratified meta-analysis and meta-regression analyses were undertaken to recognize and control the heterogeneity in meta-analysis.

^{[36]}To explore sources of heterogeneity, meta-regression analyses were performed.

^{[37]}The higher sensitivity and specificity of DBT than DM alone were consistently noted in most subgroup and meta-regression analyses.

^{[38]}We incorporated the effect size using the random-effects model in the “meta” package in R software and conducted univariate and multivariate meta-regression analyses using a mixed-effects model.

^{[39]}A meta-analysis will be conducted to calculate the pooled effect size of each outcome, and meta-regression analyses will investigate whether intervention features and the presence and absence of individual BCTs in interventions are associated with intervention effectiveness.

^{[40]}The effects of potential moderators were investigated by both subgroup and meta-regression analyses.

^{[41]}Our subgroup and meta-regression analyses indicated that prior exposure to ICIs may reduce the incidence of COVID-19 in metastatic cancer patients.

^{[42]}Given significant heterogeneity across studies, we conducted meta-regression analyses with sample characteristics, age, number of treatment sessions, treatment duration, intervention type, control group type, and study design.

^{[43]}In subgroup and meta-regression analyses, areas, NOS, mean fluorescence intensity (MFI) cut off, primary diseases, HSCT types, graft sources, and pre-transplant desensitization did not affect the impact of anti-HLA DSAs on primary graft failure (PGF).

^{[44]}Sources of heterogeneity were explored through subgroup and meta-regression analyses.

^{[45]}Meta-regression analyses found increasing GM atrophy in the right insula associated with the longer mean abstinence duration of the samples in the studies in our analysis.

^{[46]}Subgroup and meta-regression analyses were performed to assess for heterogeneity.

^{[47]}Meta-regression analyses were conducted to estimate regression coefficients.

^{[48]}Subgroup and meta-regression analyses explored potential sources of heterogeneity in the data.

^{[49]}Subgroup and random effects meta-regression analyses will be used to further investigate the potential sources of heterogeneity.

^{[50]}

## random effects model

The meta-analysis and meta-regression analyses were fit using a random effects model.^{[1]}Pooled mean differences were calculated using a random-effects model, followed by sensitivity and meta-regression analyses.

^{[2]}We incorporated the effect size using the random-effects model in the “meta” package in R software and conducted univariate and multivariate meta-regression analyses using a mixed-effects model.

^{[3]}Random-effects model meta-analyses and meta-regression analyses were used to generate summary estimates and explored sources of heterogeneity.

^{[4]}A random effects model was used to synthesise results, and heterogeneity between studies examined by subgroup and meta-regression analyses considering patient and study related variables.

^{[5]}A meta-analysis of effect sizes using the random-effects models was performed, and meta-regression analyses were performed to explore heterogeneity.

^{[6]}We calculated the pooled relative risks with 95% CIs using random effects models, and then performed subgroup and meta-regression analyses.

^{[7]}Random effects model using the method of DerSimonian and Laird were fitted, and forest plot with respective ratio estimates and 95% confidence interval (CI) for each race category, and subgroup meta-regression analyses and the overall pooled ratio estimates for prevalence, hospitalisation and mortality rate were presented.

^{[8]}A random effects model with Q statistics is used to conduct heterogeneity and publication bias between studies and meta-regression analyses were carried out to examine the effects of age, illness severity, illness duration, and scanner field strength.

^{[9]}We computed the aggregate prevalence and pooled odds ratio (OR) using the random-effects model and used meta-regression analyses to explore the sources of heterogeneity.

^{[10]}

## subgroup meta regression

Sensitivity analyses will be performed as well as a priori subgroup, meta-regression and multiple meta-regression analyses.^{[1]}Sensitivity analyses will be performed as well as a priori subgroup, meta-regression and multiple meta-regression analyses.

^{[2]}Sensitivity analyses will be performed as well as a priori subgroup, meta-regression and multiple meta-regression analyses.

^{[3]}