## What is/are Meta Analysis Model?

Meta Analysis Model - We used meta-analysis models to obtain the pooled two-dose VE, and the studies were divided into subgroups and analysed according to whether or not it was an outbreak investigation and its NOS score.^{[1]}Data were extracted from eligible studies and pooled in a meta-analysis model using RevMan5.

^{[2]}The Strengthening the Reporting of Observational Studies in Epidemiology statement was used to conduct methodological quality assessment, and a random-effect meta-analysis model was applied to estimate the prevalence.

^{[3]}We implement these models in R using JAGS and we compare our approach to the one-stage dose–response meta-analysis model in a simulation study.

^{[4]}Study data were extracted and pooled as mean difference (MD) in the meta-analysis model.

^{[5]}Pooled proportions were calculated using a random-effects meta-analysis model.

^{[6]}Pooled LTBI prevalence among adults with HBV was calculated using a random-effects meta-analysis model.

^{[7]}We subsequently took 20 draws from the probability distribution of the latent class for each arm, entered each draw into a network meta-analysis model and combined findings using Rubin's rules.

^{[8]}The pooled outcomes were calculated by TP (true positive), FP (false positive), FN (false negative), TN (true negative) by using bivariate meta-analysis model in STATA 14.

^{[9]}The random-effects meta-analysis model was employed to estimate the pooled proportion of good HTN SCPs.

^{[10]}Random-effects meta-analysis models were used to estimate the odds ratios (OR), and heterogeneity associated with the outcomes.

^{[11]}All eligible data were collected using a pre-designed data extraction form, and the overall prevalence was estimated using a random-effects meta-analysis model.

^{[12]}Lastly, a random-effects meta-analysis model was fitted to estimate the pooled proportion of knowledge, attitude, and prevention practices toward COVID-19 in Ethiopia.

^{[13]}Risk estimates of each relevant outcome were pooled as a hazard ratio (HR) with a 95% confidence interval (CI) using the random-effects meta-analysis model.

^{[14]}Data on prevalence was transformed using the logit transformation for pooling the proportions using the DerSimonian-Laird meta-analysis model.

^{[15]}The main multilevel meta-analysis model including all effects sizes (262 across 12 clusters) revealed that participants tended to underpredict the number of repetitions to task failure by 0.

^{[16]}We used a random effects meta-analysis model to generate the pooled OR and 95% CIs.

^{[17]}We applied a frequentist, random-effects Network Meta-Analysis model to pool effect sizes across trials using standardized mean differences (SMD, g) and rate ratios (RR) with their 95% confidence intervals.

^{[18]}Effect estimates were pooled using a DerSimonian and Laird random-effects meta-analysis model.

^{[19]}Random effects meta-analysis models were used to estimate pooled odds ratios (pOR) for LBW, PTB, and stillbirth between adolescent and adult pregnant women.

^{[20]}Risk estimates of each relevant outcome were pooled as a hazard ratio (HR) with a 95% confidence interval (CI) using the random-effects meta-analysis model.

^{[21]}Random effects meta-analysis models were fitted to merge study-specific risk estimates into summary relative risk (SRR) and corresponding 95% confidence intervals (CI).

^{[22]}The association of physical activity with survival was evaluated by combining study-specific hazard ratios with random-effects meta-analysis models.

^{[23]}Data on implant survival was extracted from all the included studies (single arm and comparative) to calculate point estimates with 95% confidence intervals (CI) and pooled using the DerSimonian–Laird meta-analysis model.

^{[24]}The random effects meta-analysis model was employed to pull the prevalence of lost to follow-up.

^{[25]}Relative risk (RR) and the 95% confidence intervals (CI) were calculated using either the random-effects model or the fixed-effects meta-analysis model, based on the assessment of heterogeneity.

^{[26]}The random-effects meta-analysis model was computed to estimate the pooled prevalence of undernutrition among adult tuberculosis patients.

^{[27]}Random-effects meta-analysis models were used to report pooled results.

^{[28]}Prevalence of antimicrobial resistance and virulence factors of UPEC were estimated using random-effects meta-analysis model.

^{[29]}Pooled prevalence estimates were obtained using random effects meta-analysis models.

^{[30]}1%) did not specify the primary outcome; most of the meta-analyses reported that a measure of statistical heterogeneity was used to justify the use of a fixed-effect or random-effects meta-analysis model (n=114, 58.

^{[31]}To estimate effect size, we used a random-effects meta-analysis model.

^{[32]}Random-effects meta-analysis model was used to obtain pooled estimates of mortality and 95% confidence intervals.

^{[33]}CONCLUSIONS Treatment hierarchy generated by an arm-based network meta-analysis model suggested that tunnel and laterally positioned flap, both in combination with connective tissue graft, may provide the greatest mean root coverage in the treatment of mandibular anterior recessions.

^{[34]}Pooled prevalence was calculated to estimate the prevalence of cerebral palsy among 0-18 years old and different geographical regions in China, using a random-effects meta-analysis model.

^{[35]}This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

^{[36]}We also demonstrate how, if individual-participant data (IPD) are available, the Bayesian meta-analysis model can adjust for multiple participant-level covariates, these being measured with or without measurement error.

^{[37]}Data on the visual analog scale (VAS), opioid requirements, hospital stay, and patients' satisfaction weexretr acted and pooled as standardized mean difference (SMD) with the corresponding 95% confidence intervals (CI) in the meta-analysis model.

^{[38]}The robust variance estimation based meta-analysis models were used to synthesize all the effect sizes.

^{[39]}Prevalence of antimicrobial resistance and virulence factors of UPEC were estimated using random-effects meta-analysis model.

^{[40]}Data were pooled and a random effect meta-analysis model was fitted to provide the prevalence of under nutrition.

^{[41]}We extracted the available data from included studies and pooled them in a meta-analysis model using RevMan software.

^{[42]}Odds ratio (OR) with their 95% confidence interval (CI) were pooled in a random or fixed meta-analysis model.

^{[43]}Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) with a summary receiver-operating characteristic curve were calculated using a bivariate random-effect meta-analysis model.

^{[44]}Two techniques were compared using direct comparison meta-analysis model.

^{[45]}The patterns of heat associated OI risk were investigated in different climate zones (according to Köppen Geiger classification) based on the study locations and were estimated using random-effects meta-analysis models.

^{[46]}Study quality was assessed by the Quality Assessment for Studies of Diagnostic Accuracy-2, and sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (dOR), and their corresponding 95% confidence intervals (CIs) were calculated using a bivariate random-effect meta-analysis model.

^{[47]}Location-specific association was pooled using a multivariate meta-analysis model, and attributable fraction in the current time and mortality risk from CHEs under different climate change scenarios (RCP 2.

^{[48]}Study characteristics and relative risk (RR) estimates were extracted from each article and pooled using the random-effects meta-analysis model.

^{[49]}A random-effect meta-analysis model was used to produce the pooled prevalence estimates.

^{[50]}

## 95 % confidence

Risk estimates of each relevant outcome were pooled as a hazard ratio (HR) with a 95% confidence interval (CI) using the random-effects meta-analysis model.^{[1]}We applied a frequentist, random-effects Network Meta-Analysis model to pool effect sizes across trials using standardized mean differences (SMD, g) and rate ratios (RR) with their 95% confidence intervals.

^{[2]}Risk estimates of each relevant outcome were pooled as a hazard ratio (HR) with a 95% confidence interval (CI) using the random-effects meta-analysis model.

^{[3]}Data on implant survival was extracted from all the included studies (single arm and comparative) to calculate point estimates with 95% confidence intervals (CI) and pooled using the DerSimonian–Laird meta-analysis model.

^{[4]}Relative risk (RR) and the 95% confidence intervals (CI) were calculated using either the random-effects model or the fixed-effects meta-analysis model, based on the assessment of heterogeneity.

^{[5]}Random-effects meta-analysis model was used to obtain pooled estimates of mortality and 95% confidence intervals.

^{[6]}Odds ratio (OR) with their 95% confidence interval (CI) were pooled in a random or fixed meta-analysis model.

^{[7]}We pooled continuous outcomes as mean difference and dichotomous outcomes as risk ratio with 95% confidence interval under the fixed-effects meta-analysis model.

^{[8]}The risk ratios (RR) with the respective 95% confidence intervals (CIs) of different psoriasis scores were pooled in a meta-analysis model, using the Mantel-Haenszel method.

^{[9]}The random effect meta-analysis model was used andreported as mean difference (MD) and 95% confidence interval (CI), risk of bias through RoB2.

^{[10]}We used a random-effects meta-analysis model with 95% confidence intervals with I2 statistics for heterogeneity.

^{[11]}Season-specific relative risks and 95% confidence intervals (CI) were pooled into summary risk ratio (SRR) through random-effects meta-analysis models.

^{[12]}The overall postoperative change in Hertel exophthalmometry was calculated by random-effect meta-analysis model with 95% confidence interval (CI).

^{[13]}A random effects meta-analysis model with 95% confidence interval was computed to estimate the pooled effect size (i.

^{[14]}

## pooled odds ratio

Random effects meta-analysis models were used to estimate pooled odds ratios (pOR) for LBW, PTB, and stillbirth between adolescent and adult pregnant women.^{[1]}A random-effect meta-analysis model was used to compute pooled odds ratio of the association between parity and cervical cancer.

^{[2]}We used fixed and random-effects meta-analysis models to estimate the pooled prevalence, pooled odds ratio (OR) and 95% confidence intervals.

^{[3]}To obtain the pooled odds ratio (OR), extracted data were fitted in a random-effects meta-analysis model.

^{[4]}The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were obtained by means of random-effects meta-analysis models.

^{[5]}Pooled odds ratios (ORs) and 95% confidence intervals were estimated using a random-effects meta-analysis model for included studies.

^{[6]}

## weighted mean difference

The overall effect was presented as the weighted mean difference (WMD) at 95 % confidence interval (CI) in a random-effects meta-analysis model.^{[1]}The weighted mean differences (WMD) and 95% confidence interval (CI) between the groups were calculated for each study and combined using a random effects meta-analysis model.

^{[2]}A random-effects meta-analysis model was implemented and a weighted mean difference (WMD) and 95% confidence interval (CI) were calculated.

^{[3]}Using random-effects meta-analysis models to pool study estimates, women with GDM had significantly higher (by 20%) TG levels, with a pooled weighted mean difference between GDM and non-GDM pregnancies of 0.

^{[4]}We identified studies through a systematic review of PubMed, Cochrane Library, and Embase and used a random effects meta-analysis model to synthesize estimates of weighted mean differences or combined effect size.

^{[5]}

## diagnostic odds ratio

Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) with a summary receiver-operating characteristic curve were calculated using a bivariate random-effect meta-analysis model.^{[1]}Study quality was assessed by the Quality Assessment for Studies of Diagnostic Accuracy-2, and sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (dOR), and their corresponding 95% confidence intervals (CIs) were calculated using a bivariate random-effect meta-analysis model.

^{[2]}Using a bivariate mixed-effects meta-analysis model, the pooled Sen, specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) with 95% confidence interval values of total miRNAs were 0.

^{[3]}

## random effects model

Random effects model was chosen as the meta-analysis model for the research, and Fisher Z value was used in calculating the effect size value.^{[1]}A random-effects model network meta-analysis model was used to analyze the relation between antidepressants and VA/SCD.

^{[2]}, a two-step method), 2) a standard random-effects model after randomly choosing one effect size per paper, 3) a multilevel (hierarchical) meta-analysis model, modelling paper identity as a random factor, and 4) a meta-analysis making use of a robust variance estimation method.

^{[3]}

## corresponding 95 %

Random effects meta-analysis models were fitted to merge study-specific risk estimates into summary relative risk (SRR) and corresponding 95% confidence intervals (CI).^{[1]}Data on the visual analog scale (VAS), opioid requirements, hospital stay, and patients' satisfaction weexretr acted and pooled as standardized mean difference (SMD) with the corresponding 95% confidence intervals (CI) in the meta-analysis model.

^{[2]}

## robust variance estimation

The robust variance estimation based meta-analysis models were used to synthesize all the effect sizes.^{[1]}The robust variance estimation based meta-analysis models were used to synthesize all the effect sizes.

^{[2]}