## What is/are Group Analysis?

Group Analysis - The VSTs in SARS-CoV-2 infections based on different demographic and clinical characteristics, treatments and specimens were stratified by subgroup analysis.^{[1]}Further subgroup analysis was done based on the duration of illness and cycle threshold values.

^{[2]}Subgroup analysis of underweight children (BMI.

^{[3]}Dysregulated mRNA expressions of three BolA family members were significantly associated with prognosis in overall or subgroup analysis.

^{[4]}” ANOVA along with the multiway analysis are then applied to signature frequency components, their mean and standard deviation are identified as “fault signatures”; and the p-value from the inter-group analysis of the mean and standard deviation is used to classify faults.

^{[5]}For subgroup analysis, we used t-test and one-way ANOVA.

^{[6]}Female patients had a higher risk of severe dengue than male patients in the main analysis (2674 [16·2%] of 16 481 vs 3052 [10·5%] of 29 142; odds ratio [OR] 1·13 [95% CI 1·01–1·26) but not in the subgroup analysis of studies with children.

^{[7]}Using Metadataset, we show how evidence can be filtered and weighted, and results can be recalculated, using dynamic methods of subgroup analysis, meta-regression, and recalibration.

^{[8]}It can also compress the SVG files with a large number of SNPs and support subgroup analysis.

^{[9]}Subgroup analysis on HBeAg status and level of HBV DNA demonstrated that evolution of proportion of achieving CVR was not significantly different between groups.

^{[10]}In the subgroup analysis of patients with OPSCC, a total of 57 patients in the TORS cohort, 73 in the non-robotic cohort, and 171 in the registry cohort were eligible for the present study.

^{[11]}Subgroup analysis in 43 MDI participants showed that the proportion reaching target HbA1c using liraglutide was significantly higher than in placebo: 9/22 (41%) vs 1/21 (5%), p = 0.

^{[12]}A subgroup analysis was performed in PP-BCPW patients that never lactated (PP-BCPW/NL), lactated ≤3 months (PP-BCPW/Lshort) or lactated >3 months (PP-BCPW/Llong).

^{[13]}Subgroup analysis was also used for identifying DD correlates.

^{[14]}Subgroup analysis by gender has revealed that the seroprevalence of CoxA16 antibody was 56.

^{[15]}Subgroup analysis of HT suggested that admission NLR levels rather than post-IVT NLR levels was associated with higher risk of HT (OR=1.

^{[16]}A subgroup analysis based on flow rate of oxygen indicated that the effects of supplemental oxygen therapy on blood pressure significantly differed.

^{[17]}systemic, loco-regional and CDT) were detected by subgroup analysis at any of these time points (tests for subgroup differences: P = 0.

^{[18]}However, significant improvement in muscle strength was found in between group analysis (p≤0.

^{[19]}Sleep quality and work condition categories were used for subgroup analysis.

^{[20]}As a subgroup analysis, we compared the mortality rates following the use of echinocandins versus liposomal amphotericin B.

^{[21]}In the subgroup analysis of cases with idiopathic scoliosis, Ponte osteotomy (OR = 6.

^{[22]}Similar results to the main analysis were also found in the subgroup analysis of intentional poisoning and females, but not in that of accidental poisoning and males.

^{[23]}Subgroup analysis of ethnicity showed that VEGF expression was still correlated with periodontitis in the Asian and European populations.

^{[24]}Sensitivity analysis, subgroup analysis and meta-regression were used to explore the sources of heterogeneity.

^{[25]}Subgroup analysis and interaction test were used to compare the difference of the primary outcome among trials that did and did not report a significant reduction in SMI- in the patients treated by CABG.

^{[26]}A subgroup analysis based on the anterior versus the posterior approach was performed.

^{[27]}In the present subgroup analysis in 5,010 patients (1,388 women and 3,622 men) with confirmed MI, we report the effect of supplemental oxygen on the composite of all-cause death, rehospitalization with MI, or heart failure (HF) at long-term follow-up, stratified according to sex.

^{[28]}In addition, subgroup analysis and meta-regression analysis were conducted to adjust potential confounders and investigate the source of heterogeneity.

^{[29]}In subgroup analysis, endocervical pathogens were further stratified into vaginal, respiratory, enteric, skin, oral, and other.

^{[30]}Subgroup analysis, which associated clinical factors, revealed that the heterogeneity of immune markers was more obvious in extrapulmonary, metachronous, and treated MTs, while fewer differences were observed in intrapulmonary, synchronous, and untreated MTs.

^{[31]}Risk of death remained elevated for PLWH in a subgroup analysis of hospitalised cohorts (HR 1.

^{[32]}

## partial least square

We used multi-group analysis (MGA) using Partial least squares structural equation modeling (PLS-SEM).^{[1]}We use partial least squares regression through PLS-3 with Multi Group Analysis to test a set of theory-based hypotheses.

^{[2]}Variance-based estimator, Partial Least Square (PLS) structural equation modelling was used to confirm the proposed structural relationship and Multi Group Analysis (MGA) was employed to test the moderating effect of price sensitivity.

^{[3]}The analysis used partial least square modeling to examine the relationship between activities, dimensions and multigroup analysis to estimate potential significant differences in group-specific parameters, which are not often used in destination analysis, ensuring rigor in the data analysis and model.

^{[4]}Partial Least Square – structural equation modelling (PLS-SEM) and multigroup analysis (MGA) was utilized to perform the analysis.

^{[5]}For the analysis of the data obtained, the partial least squares (PLS) regression technique and the Multigroup Analysis were used.

^{[6]}The study also applied the Multi-Group Analysis-Partial Least Squares (MGA-PLS) analysis permutation to test students’ satisfaction.

^{[7]}The responses (n = 395) of those who traveled internationally within five years were analyzed utilizing partial least squares-structural equation modeling (PLS-SEM) with multi-group analysis.

^{[8]}Design/methodology/approach A total of 337 samples were collected from the Islamic banking practitioners in the United Arab Emirates using a purposive sampling technique, and the empirical analysis was conducted with the measures of model fit and bootstrapping technique using Partial least square Structural equation modelling and multi-group analysis.

^{[9]}Questionnaires were used to collect data which was analyzed using Partial Least Squares-Multi Group Analysis.

^{[10]}The statistical analysis employs partial least squares structural equation modelling (PLS-SEM) methods such as PLS algorithm, model assessments, PLS predict, PLS goodness-of-fit, invariance assessment and multi-group analysis, and importance-performance map analysis.

^{[11]}and analyzed through partial least squares structural equation modeling (PLS-SEM) and multi-group analysis.

^{[12]}Data from 69 hotels in four Angolan provinces were analyzed using the partial least squares (PLS) approach and multi group analysis.

^{[13]}Partial least squares structural equation modelling was used to test the study hypotheses and multigroup analysis (MGA) between industrial sectors.

^{[14]}A total of 501 responses were analyzed with smart partial least squares to run a multigroup analysis.

^{[15]}Using a sample of 1499 rice farmers in China, the partial least squares structural equation modeling (PLS-SEM) was adopted for empirical analysis, and the Multi-group Analysis (MGA) was employed to examine the heterogeneity among farmers’ socio-economic status.

^{[16]}Design/methodology/approach Partial least squares structural equation modeling was used to conduct multigroup analysis for the two sectors.

^{[17]}Data were analyzed using partial least squares structural equation modeling (PLS-SEM) and partial least squares multi-group analysis (PLS-MGA).

^{[18]}A more advanced partial least-square method of structural equation model (PLS-SEM) and a multiple-group analysis (MGA) model are applied to estimate the effects and heterogeneities of these factors on low-carbon travel behavior intention among three cities and four age groups.

^{[19]}The data was analyzed using the concept of Hierarchical Component Model (HCM) for TOE constructs and Multi-Group Analysis (MGA) under the purview of Partial Least Square Structural Equation Modeling (PLS-SEM).

^{[20]}Partial least squares multigroup analysis (PLS-MGA) was used to examine the moderating effect of education.

^{[21]}Future studies should also utilise the qualitative approach or employ the Partial Least Square-Multi-Group Analysis (PLS-MGA) to examine whether ethnicity, working tenure, and working locations play an essential role in the relationship between employee engagement and normative commitment.

^{[22]}Partial least squares-multi-group analysis (PLS-MGA) was employed to examine whether there are significant differences among various ethnic groups.

^{[23]}We used partial least squares modeling and multigroup analysis to compare consumers' culture-driven responses to crisis.

^{[24]}Partial least squares structural equation modelling was used to analyse the data, and multi-group analysis was conducted to examine the moderation effects.

^{[25]}Partial least square structural equation modeling (PLS-SEM) is used to analyze the data and multi-group analysis is conducted to investigate the role of perceived CSR significance.

^{[26]}A partial least-squares regression was used for the statistical analysis and was performed using a data group analysis.

^{[27]}Partial least squares structural equation modelling was used to conduct a multigroup analysis on a sample of 473 consumers (n = 266 from Spain, n = 207 from Italy).

^{[28]}Partial least squares – structural equation modeling and multi-group analysis were used to test the model and hypotheses.

^{[29]}Partial least squares structural equation modeling and multi-group analysis were conducted to test the hypotheses.

^{[30]}Then, to test for causal significance amongst relationships and differences at the path level, the partial least square method and multigroup analysis (MGA) was undertaken.

^{[31]}Design/methodology/approach A total of 335 samples were collected from the sales professionals of IFIs in Malaysia using a purposive sampling technique and the empirical analysis was conducted with the measures of model fit and bootstrapping technique using partial least square structural equation modeling and multi-group analysis.

^{[32]}Metode analisis yang digunakan yaitu Partial Least Square dan pengujian untuk pengaruh dari variabel moderator menggunakan Partial Least Square-Multi Group Analysis (PLS-MGA).

^{[33]}

## statistically significant difference

Regarding viscoelastic properties, in the intra-group analysis we found statistically significant differences in the experimental limb at T1, decreasing muscle stiffness in gluteus maximus (p < 0.^{[1]}In the intergroup analysis, no statistically significant differences were found among the groups (p > 0.

^{[2]}Results: Within-group analysis has shown there is a statistically significant difference (p<0.

^{[3]}Intergroup analysis in groups D, C and F for the onset of sensory blockade, onset of complete sensory blockade and duration of complete sensory blockade in three different groups noted a statistically significant difference.

^{[4]}Between-group analysis showed no statistically significant differences.

^{[5]}Subgroup analysis showed no statistically significant differences between the incidence of otolaryngology symptoms in severely ill patients and non-severely ill patients (OR 1.

^{[6]}In the subgroup analysis of the study group, FPG was with statistically significant differences (P=0.

^{[7]}This suggested that no statistically significant differences existed in the fucoxanthin antioxidant property extracted from two subtypes, which was consistent with the results from the subgroup analysis and meta-regression.

^{[8]}Furthermore, between-group analysis showed a statistically significant difference between the intervention and placebo groups in this regard.

^{[9]}The operative costs of LLR were significantly higher than those of OLR, while subgroup analysis also showed higher operative costs in the LLR group for major hepatectomy, but no statistically significant difference for minor hepatectomy.

^{[10]}In the subgroup analysis: there were no statistically significant differences in the response to bDMARD at 6 and 12 months evaluated by ASDAS response and ASAS 20, 40 and 70 responses according to the baseline 25-OHvitD levels (25-OHvitD <20ng/mL vs ≥20ng/mL; 25-OHvitD <30ng/mL vs ≥30ng/mL); and there were no statistically significant differences in the baseline 25-OHvitD levels at baseline according to the response to bDMARD at 6 and 12 months of bDMARD (ASDAS: no response vs clinically important improvement or major improvement; ASAS 20: no response vs response).

^{[11]}In the subgroup analysis according to invasion anomaly, the level of YKL-40 in invasion-positive group (n=11) was higher than invasion-negative group (n=22), indicating a statistically significant difference.

^{[12]}When subgroup analysis was performed according to the residual tumor amount, we could not find any statistically significant difference in both PFS and OS in terms of SLND and lymph node involvement in R0 (complete resection) group (p = 0.

^{[13]}Subgroup analysis showed that there were statistically significant differences in N(F=9.

^{[14]}Subgroup analysis for only primary infertility patients showed a statistically significant difference in CPR between TAI positive and TAI negative groups.

^{[15]}A subgroup analysis to evaluate T2 second-line Tofacitinib therapy showed no statistically significant differences in any response criteria according to the number of previously received biologicals.

^{[16]}A subgroup analysis of lesion site, photosensitizer, laser type, radiant exposure, and power density revealed no statistically significant differences.

^{[17]}In the subgroup analysis, a statistically significant difference was determined between Group 1 and Groups 3, 4, 5, and 6 (p = 0.

^{[18]}RESULTS The between group analysis indicated a statistically-significant difference in the mean score of patient activation (P < 0.

^{[19]}When a subgroup analysis was performed, a statistically significant difference was observed in the median PAPP-A between ART/PGT-A/FET group versus ART/no PGT-A/FET group (p =.

^{[20]}For intragroup analysis, no statistically significant difference in the percentage of debris elimination was noted between 3 mm and 5 mm in all four groups.

^{[21]}RESULTS The intergroup analysis revealed no statistically significant differences in age, angina pectoris class, level of arterial pressure between the groups.

^{[22]}Subgroup analysis showed no statistically significant differences between IL-17 and TNF-a inhibitors, but the IL-17 inhibitor class had a higher SUCRA values.

^{[23]}At two years, follow-up postoperative intergroup analysis showed no statistically significant difference between groups.

^{[24]}An overall improvement in MAS of spastic elbow flexors was observed during the 3-week visit ([Formula: see text]), yet no statistically significant difference found with intra-group or inter-group analysis.

^{[25]}In subgroup analysis, there was no statistically significant difference between overall, 30-day, 90-day or in-hospital mortality (P = 0.

^{[26]}For the foam roller and control groups, the between-group analysis revealed a statistically significant difference in gastrocnemius stiffness and ankle dorsiflexion ROM after intervention (p<0.

^{[27]}00001), but subgroup analysis showed that there was no statistically significant difference between acupuncture and sham acupuncture in improving cure rate (OR =10.

^{[28]}

## body mass index

We also performed an in-group analysis of PF patients in terms of age, sex, body mass index, and duration of symptoms.^{[1]}Subgroup analysis according to body mass index and relative weight loss were performed to investigate the effect of weight loss on pregnancy and live birth outcomes.

^{[2]}Subgroup analysis was also conducted stratifying by gender, study location, study design, source of noise, study quality, adjusting for smoking, drinking, body mass index, physical activity and shift work.

^{[3]}There are several factors that were used for subgroup analysis such as age, body mass index (BMI), method for measuring malondialdehyde (MDA), and disease severity.

^{[4]}The adjusted OR after subgroup analysis for body mass index (BMI) was as follow, BMI > 25: 0.

^{[5]}Methods Post hoc subgroup analysis of critically ill patients with obesity (body mass index ≥ 30 kg·m −2 ) from a multicenter randomized controlled trial comparing preoxygenation with noninvasive ventilation and high-flow nasal oxygen before intubation of patients with acute hypoxemic respiratory failure (PaO 2 /FiO 2 < 300 mm Hg).

^{[6]}In addition, results of subgroup analysis revealed a significant reduction in IL‐6 and TBARS concentrations when the baseline body mass index (BMI) of participants was lower than 25 kg/m2.

^{[7]}Subgroup analysis showed no increase in the rate of procedure-related complications in patients with elevated body mass index.

^{[8]}Subgroup analysis suggested that these associations were more evident in elderly with lower body mass index and dietary calcium intake less than 400 mg/d.

^{[9]}In subgroup analysis according to tumor–node–metastasis stage, the overall survival of the high C-reactive protein-to-body mass index ratio group was significantly shorter than that of the low C-reactive protein-to-body mass index ratio group (P < 0.

^{[10]}A post hoc subgroup analysis of changes in BP and heart rate was performed depending on age, sex and baseline body mass index.

^{[11]}In addition, subgroup analysis was carried out to avoid deviation according to the body mass index (BMI).

^{[12]}A weight loss group (n=25) and a non-weight loss group (n=19) were divided; while subgroup analysis according to body mass index and stratification analysis according to weight loss proportion were performed to investigate the effect of weight loss on pregnancy and livebirth outcomes.

^{[13]}Subgroup analysis was performed according to geographic region, type of urinary incontinence (UI), severity of UI, age, and body mass index (BMI).

^{[14]}In the subgroup analysis, most potential confounding variables did not influence the association between NAFLD and DM risk after PSM, except for body mass index (P for interaction=0.

^{[15]}We performed a subgroup analysis of studies after matching the age and body mass index (BMI), as well as according to the severity of OSA.

^{[16]}Subgroup analysis demonstrated that diabetic patients with body mass index ≥25 (odds ratio = 0.

^{[17]}A subgroup analysis for the body mass index (BMI) was performed.

^{[18]}We conducted subgroup analysis of patients with diabetes, an American Society of Anesthesiologists (ASA) score ≥3 and body mass index (BMI) ≥30kg/m2.

^{[19]}A subgroup analysis was performed to explore the treatment effect by body mass index (BMI).

^{[20]}Influence of each on body mass index (BMI) z-score change was tested in a pooled group analysis and then compared by treatment group.

^{[21]}In the subgroup analysis of age, gender, body mass index (BMI), white blood cell (WBC), beta-block, and revascularization, the association between midazolam using and increased 28-days mortality remained significantly.

^{[22]}Subgroup analysis should involve basal calcium intake, age, sex, basal blood pressure, and body mass index.

^{[23]}Subgroup analysis showed that the CYP2C19 genotype prognostic value was present in the following subgroups: male, age >60 years, body mass index (BMI) >24 kg/m2, SYNTAX score >15, current smokers, and patients without chronic kidney disease.