Abstract
- This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using Stata software under frequentist framework. The NMA tries to synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions—similarity, transitivity, and consistency—should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus, NMA should be activated in order to guarantee the quality of healthcare system.
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Keywords: Network meta-analysis, Treatment outcome, Mixed treatment comparison, Biostatistic
INTRODUCTION
- As newly developed drugs conducted the third stage of randomized clinical trials (RCT) are approved for marketing, and enter into the armamentarium of treatment, there is a need for comparative effectiveness research (CER) to evaluate the effectiveness of drugs used for the same treatment goal and meta-analysis to synthesize the results of the CER [1]. Conventional meta-analysis on the treatment effects of new drugs is conducted on the effect size based on pairwise head-to-head direct comparison, but data from direct comparisons are relatively limited [2]. In contrast, the necessity for indirect comparisons among various drugs of the same efficacy used in clinical practice became greater [3,4]. Accordingly, an analytic approach called network meta-analysis (NMA) was developed to include in the meta-analysis not only direct comparisons, but also indirect comparisons based on logical inference; in the latter case, no comparisons are actually performed [3,5-10].
- Statistical approaches to NMA are largely classified as frequentist and Bayesian frameworks [7]. Because part of NMA has indirect, multiple comparisons, Bayesian framework seems logically more valid, and 60-70% of NMA studies have taken a Bayesian approach [6,11-13]. However, if the prior probability is not established in the study hypothesis, Bayesian analysis poses many limitations for ordinary researchers using NMA because the problem of establishing prior probability is rather more complex than the problem of testing the research hypothesis, that is, the original purpose of the analysis [7]. In 1997, Bucher et al. [14] proposed an NMA approach based on the frequentist framework using random-effects models. Since then, many methodological developments have taken place [7,15], and articles introducing SAS (https://www.sas.com/en_us/home.html) and Stata (http://www.stata.com) program commands have been presented [12,15,16].
- From 2008 onwards, the number of publications based on NMA increased at a rapid pace [2-4,15]. In 2011, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) defined the concepts related to NMA and established guidelines relating to methodological and statistical issues to help researchers conduct NMA in a valid manner [7,17]. Furthermore, ‘PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Network Meta-Analysis checklist’, as a guideline for reporting NMA research outcomes, was also developed [7,18].
- As NMA has features of multivariate multi-level analysis fundamentally, clinical researchers, as well as epidemiologists and statisticians, should understand them sufficiently well to perform NMA [19]. Hence, in this article, the terms used in NMA are defined, relevant statistical concepts are summarized, and the NMA analytic process based on the frequentist framework is illustrated using Stata program and an actual example.
INTRODUCTION OF RELATED CONCEPTS
- Definition of terms
- Since Bucher et al. [14] proposed the concept of indirect treatment comparison (ITC) on treatment effect in 1997, such terms as ‘multiple treatment comparison’ meaning comparison of several treatments [20] and ‘mixed treatment comparison’ (MTC) meaning a combination of direct and indirect comparisons [6] have been introduced.
- However, at present, NMA is primarily used to mean a research effort to synthesize the results obtained by comparing several studies which examined multiple treatments [3,5,7,16,21]. ISPOR [7] defines NMA as a comparison of the effectiveness of 2 or more treatments, and categorizes the comparison type as MTC if the network geometry shows a closed loop and ITC if it does not (Figure 1).
- Relevant assumptions
- Meta-analysis is a statistical methodology to synthesize the results of several studies, and overall effect size is valid only if various a priori assumptions are satisfied [22]. Furthermore, NMA requires more strict methodological, logical, and statistical assumptions [23,24], about similarity, transitivity, and consistency, respectively [5,12,17,19,25,26]. In NMA, whether each of these is satisfied must also be examined [8,10,19].
Similarity
- To compare among the clinical trial studies extracted for analysis, similarity in the methodology used in the studies must be assumed [5,27]. Similarity is qualitatively assessed on each of the selected articles from a methodological point of view, and is not a hypothesis to be tested statistically. To examine similarity, the population, intervention, comparison, and outcome (PICO) technique is used [26]. Specifically, similarity among the studies extracted for analysis is examined for the following 4 items: clinical characteristics of study subjects, treatment interventions, comparison treatments, and outcome measures. If the similarity assumption is not satisfied, not only are the other two assumptions negatively affected [9], but there is also a need to check for the heterogeneity error [10,23].
Transitivity
- Transitivity covers the validity of the logical inference, while similarity relates to the methodological feasibility of comparing. To explain simplistically, if direct comparisons of 3 drugs—A, B, and C—treating the same illness found that A was more effective than B, and B was more effective than C, then A can be expected to be more effective than C, even though the two were never directly compared. Such transitivity should be satisfied for all cases in an NMA [4,5,28].
- If the researcher compares the outcomes of direct and indirect comparisons according to logical inference, the satisfaction of the transitivity assumption can be examined objectively. Statistical assessment of the outcomes of direct and indirect comparisons is called consistency [4,5,10]. If inconsistency is observed, non-transitivity should be suspected [29]. If non-transitivity is suspected, the presence of effect modifiers influencing treatment effect should first be examined [9,27,30].
Consistency
- Consistency, an objective measure of transitivity, means that the comparative effect sizes obtained through direct and indirect comparisons are consistent. Hence, consistency is statistically examined transitivity [5,7,24,31], and some researchers call it coherence [8,21,32]. For this reason, the assumptions of transitivity and consistency can be regarded as similar [9,33,34]. However, the authors of this article differentiate transitivity from consistency to emphasize that the perspectives used are logical and statistical, respectively.
- According to a summary of techniques to check the consistency assumption utilized in the existing NMA studies [2], the techniques are categorized as those evaluating the fitness of statistical models [34,35] and those assessing the logical structure of graphs [29]. The model [Y] presented in the Stata program simultaneously considers the level of heterogeneity [H] (which should be examined in any meta-analysis) and the level of inconsistency [C] (for conducting multiple comparisons within a network), as well as the size of the treatment effect [D] of interest [16]. The equation that considers within-study variance (E), Y= D+H+C+E, is called the inconsistency model. If the level of inconsistency is zero, that is, [C= 0], it is considered a consistency model.
- Consistency is statistically evaluated based on the confidence interval of the difference in comparative effect size between direct and indirect comparisons [10,32], and inconsistency is observed in approximately 1/8 of NMA studies [23]. It is very important to identify the cause of inconsistency [1,10,23]. Ioannidis [36] proposed 4 kinds of causes of inconsistency—chance, bias in headto-head comparison, bias in indirect comparison, and genuine diversity—and Higgins et al. [35] classified the causes of inconsistency into loop inconsistency, which refers to a difference between direct and indirect comparisons, and design inconsistency, which refers to a difference due to different sets of treatments being compared.
- Stata tests for inconsistency have 2 levels [12,16]. The first is a global approach to test for overall inconsistency, in which the level of inconsistency is computed according to the type of between-treatment comparison for all cases and then the values are used to test for global linearity via the Wald test. The second is a local approach, in which each treatment is individually examined (nodesplitting) and the outcomes of direct and indirect comparisons are statistically tested.
- Researchers should pay attention to the consistency assumption and explore the presence of effect modifiers causing overall inconsistency in the global approach [12,37,38]. To examine them, sensitivity analysis is utilized [3,30], and if it is determined that an effect modifier is present, performing meta-regression is recommended to adjust the corresponding variable [3,5,38].
- Network geometry
- Network geometry is a diagram showing the interactions among the articles included in NMA [39]. The diagram provides important information in establishing analytic strategies and interpreting the results [5,8,39], and so it is strongly recommended to use network geometry in presenting the NMA analysis results [1,18]. One of NMA’s features is that network geometry may change with an addition of new research outcomes or new treatments in the comparison set [39].
ILLUSTRATION OF STATA APPLICATION
- Preparing for analysis: information extraction and network meta-analysis support program installation
- The following research question was formulated to illustrate how to perform NMA: whether transfusion rate in total hip joint replacement is different depending on the method of tranexamic acid administration. During the literature search process, 25 articles were selected and the extraction results are listed in Appendix 1. Drug administration was classified into the following 5 treatments: placebo (A); IV_single use (B); IV_double use (C); topical use (D); and a combination of IV and topical use (E).
- As shown in Appendix 1, a long form to code the sample size for each treatment group in a study is recommended because this format makes it easy to understand the commands and also makes it easy to edit data, if necessary.
- To perform NMA using Stata, the network package should first be installed [16]. Then, the variables in the analysis should be specified by typing the command < network setup d n, studyvar (study) trtvar(trt) ref(A)> . In the command, < network setup> means that network package is used for analysis. The number of events (< d> ) and the total sample size (< n> ) are entered, in this order. After a comma, the relevant options are entered; < studyvar> refers to the variable for study title; < trtvar> is the variable for treatments; and < ref> is the variable for the reference treatment among the treatments. The command corresponding to the data organized in Appendix 1 is found in Figure 2.
- According to Figure 2, the reference treatment is A (placebo). Of the 25 studies, Xie 2016 and Yamasaki 2004 included cells with d= 0, and Stata replaced them with a default value of 0.5. Consequently, 0.5 was assigned to both the intervention and control groups, which increased the sample size per treatment by 1. Also, for studies with no information on the reference treatment, A, as in North 2016 and Xie 2016, Stata generates a tiny amount of data in the reference arm as a default. This practice is called augmented method, and is advantageous because the overall effect size is not affected, errors in the equations can be reduced, and all extracted studies are utilized in the analysis.
- Step 1: generating network geometry
- The command to draw a network geometry to explore comparative relationships among treatments is < network map > , and Figure 1 shows the outcome of the current example. The size of the 5 nodes—one for each treatment—indicates the number of studies included in the corresponding nodes, while the thickness of the lines connecting 2 nodes indicates the amount of relevant data. Also, all 5 nodes were closed, which confirms that MTC analysis can be performed. To examine the contributions of individual treatments in a table form, the command < netweight> is used.
- Step 2: testing for inconsistency
- This step in NMA is to statistically test whether the consistency assumption among 3 NMA assumptions is satisfied. To check for overall inconsistency, the command < network meta inconsistency> is used for the inconsistency model provided in Stata, and Figure 3 shows the results of this case study. The p-value displayed at the bottom of Figure 3 is the result of testing for inconsistency at the overall level. Based on the p-value, the null hypothesis cannot be rejected and the consistency assumption could be accepted at the overall level of each treatment.
- Next, the command < network sidesplit all> is used for the local test on loop inconsistency. Table 1 shows the results of the local inconsistency test in this case study, listing the size of differences for each treatment and the statistical test results. None of the treatments showed statistical significance. Because inconsistency was found to be absent in both global and local tests, the consistency assumption was accepted.
- Step 3: creating plots and league table of effect size by treatment
- To display effect sizes in a plot and a league table, the outcomes should first be stored in memory using the command < network meta consistency> . There are 2 ways in NMA to graphically represent effect size by study and by treatment: network forest plot (NFP) and interval plot. To generate NFP in Stata, the command < network forest, msize (*0.15) diamond eform xlabel (0.1 1 10 100) colors (black blue red) list> is typed. The main command to generate forest plots is < network forest> , and options are specified after a comma. Among the various options, < diamond> uses a diamond shape to show summary effect sizes and < eform> generates transformed indices to make it easy to interpret the forest plot. Other options are there to help in easily visualizing the graph: < msize (*0.15)> decreases the value of individual studies’ effect size by 0.15 times; < xlabel (0.1 1 10 100)> sets the unit on the x axis; and < colors (black blue red)> sets the colors of the effect of each study within a treatment in the comparison set, the pooled effect of a treatment in the comparison set (also called “pooled within design”), and the pooled overall effect (also called “pooled overall”) as black, blue, and red, respectively (Figure 4).
- A wide range of information is obtained in an NFP. First, it provides information on the effect size of each study and each treatment. The pooled effect of each treatment in the comparison set (blue color) shows the results of the test for the inconsistency model, And the pooled overall effect (red color) shows the result of the test for the consistency model. Second, the p-value displayed in the lower left of the plot is congruent with the result of the global test on inconsistency, which confirms that the consistency is accepted. Third, heterogeneity among individual studies within a treatment can be visually inspected. Moreover, based on the similarity between the size of pooled effect of each treatment in the comparison set (blue color) and the size of pooled overall effect (red color), it can be determined whether the consistency model is supported.
- Although useful information is provided by NFP, readability suffers if the number of articles included in the analysis or treatments in the comparison set is large. In such a case, it is advised to generate interval plots, by typing the command < intervalplot, eform null (1) labels (Placebo IV_single IV_double Topical Combination) separate margin (10 8 5 10) textsize (2) xlabel (0.01 0.1 1 10)> . The main command is < intervalplot> ; < eform> transforms the original logarithmic data into indices for easier interpretation; < null (1)> inputs 1, the value indicating statistically significant difference in a ratio like odds ratio; <label> defines how treatments should be labeled; < separate> and < margin> set the ranges to generate easy-to-read plots, the values of which should be appropriately determined by the user because they vary greatly depending on the number of articles as well as the number of treatments in the comparison set. Figure 5 shows the interval plot obtained by typing the commands discussed above. It is relatively intuitive to compare the effect sizes of individual treatments and very easy to interpret the results. A network league table, shown in Appendix 2, can be created based on the outcome comparing the effect sizes of treatments, which is produced in the Stata result window after the command < intervalplot> is typed.
- Step 4: determining relative rankings of treatments
- Once comparative effectiveness of the treatments has been evaluated through the previous steps, the next step is to rank order the treatments to identify superiority [12]. In other words, the treatment interventions showing the most superior treatment effect are evaluated.
- Stata supports two commands—network rank and surface under the cumulative ranking (SCURA)—to rank order treatments. There is little difference in the outcome between the commands, but it is easier to use network rank, for which the command < network rank min, line cumulative xlabel (1/4) seed (10000) reps (10000) meanrank> is typed. The main command is < network rank> , and < min (or max)> specifies whether superiority should be determined by using ascending or descending order of effect size. In this case study < min> was used because a treatment is more effective as the effect size compared to the reference treatment (placebo, A) is smaller.
- As shown in Figure 6, the probability of treatment E (combination) being the best is approximately 98.1%, and the probability for it to be at least the second best is 99.2%. In the SCURA, the surface area for treatment E reaches almost 100%, confirming again that it is the best intervention [40]. The SUCRA command is used for more precise estimation of cumulative ranking probabilities. Based on the SUCRA results, treatment E (combination) is followed by C (IV_double), D (topical), B (IV_single), and A (placebo). A clinical interpretation of the results is that the administration of tranexamic acid, in combination with IV and topical use is recommended to ensure maximum decrease in the probability of transfusion in total hip joint replacement.
- Step 5: checking for publication bias
- To check for publication bias in NMA, a network funnel plot is created. Because the Stata network package does not directly support the generation of network funnel plots, the data should first be transformed, as demonstrated in Appendix 2. To do so, data should be generated by typing the network forest command with the list option, and the comparative effect size (diff) and standard error (se) are summarized for each pair of treatments within individual articles that are directly compared with each other (t1, t2).
- After the data shown in Appendix 3 are uploaded in a new Stata window, the command < netfunnel diff se t1 t2, random bycomparison > is typed to generate network funnel plots. Here, the main command is < netfunnel> ; < diff> is comparative effect size between treatments in logarithmic scale and < se> indicates se; < random> in the option list means the use of a random effect model, and < bycomparison> is to color code treatments. If the user wants to generate selective funnel plots with respect to placebo A, the command < netfunnel diff se t1 t2 if t1= = “A”, random bycomparison> is typed. Once the plots are generated, publication bias is visually inspected using the criterion of symmetry. It is advised to consider performing sensitivity analysis, if necessary.
DISCUSSION AND SUGGESTIONS
- So far, the application of NMA within frequentist framework has been demonstrated. Below, we list points for researchers to consider when performing NMA and suggestions for those points.
- First, NMA is for direct and indirect comparisons in a set of treatments and it is limited to the results of RCT studies [5]. At the current advancement of the analytic technique, it is not recommended to apply NMA to the results of observational research like cohort study and case-control study. Also, the fact that researchers, funding agencies, the types of drugs approved for marketing in different countries, and the research ethics committees are operated, etc., could affect the RCT research issues and outcomes, should be factored in [41].
- Second, the 3 assumptions to be satisfied in NMA should be examined meticulously [25,42]. Of the assumptions, practical decisions from a clinician point of view should be made on the similarity and transitivity assumptions [42]. If inconsistency is concluded via statistical testing, effect modifiers should be identified by utilizing network geometry to thoroughly investigate the relationships among the selected articles [37]. Then, NMA meta-regression should be performed and the results before and after the adjustment with the effect modifiers should be evaluated in order to derive clinically valid conclusions [5,31].
- Third, the presence of bias due to small-scale studies should be considered [8]. Studies with a small number of subjects can not only cause publication bias [43], but also generate a relatively large treatment effect [44]. Therefore, it is recommended to evaluate publication bias by using a random effect model [7] and performing sensitivity analysis [3,30].
- Fourth, the application of Bayesian framework may be considered, as limitations exist in NMA based on frequentist framework [6,7]. If the ultimate goal of an NMA study is to make a medical decision and predict the outcome under uncertainty, Bayesian framework is more appropriate [11-13]. At present, several statistical programs supporting Bayesian analysis are available, including WinBUGS (https://www.mrc-bsu.cam.ac.uk/software/bugs/) [12].
- Evidence synthesized through a systematic review based on meta-analysis is the most powerful with respect to scientific persuasion [45]. Moreover, the outcomes of NMA—an analysis that synthesizes comparative effectiveness through direct and indirect comparisons among treatment interventions with the same treatment goal and rank orders them—have great significance in evidence-based decision making in health care [3,5,46]. Thus, the use of NMA should be facilitated to enhance the quality of health care in Korea, and it is expected that this article will motivate Korean researchers to facilitate the application and practice of NMA.
ACKNOWLEDGEMENTS
This article is an outcome of the research activities of the Korean Meta-Analysis Study (President: In-Soo Shin).
NOTES
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The authors have no conflicts of interest to declare for this study.
SUPPLEMENTARY MATERIAL
Supplementary material (Korean version) is available at http://www.e-epih.org/.
Figure 1.Network geometry. When the obejctive is to examine comparative effectiveness of B-C among the treatments A, B, and C, a closed loop is present if research data comparing all 3 pairs (A-B, B-C, and A-C) exist.
Figure 2.Results of network setup order.
Figure 3.Results of test for inconsistency.
Figure 4.Network forest plot. A, placebo; B, IV_ single; C, IV_double; D, topical; E, combination.
Figure 5.Interval plot. CI, confidence interval.
Figure 6.Results of network rank test. A, placebo; B, IV_single; C, IV_double; D, topical; E, combination; SCURA, surface under the cumulative ranking.
Table 1.Inconsistency test between direct and indirect treatment comparisons in mixed treatment comparison
Side |
Direct
|
Indirect
|
Difference
|
p>z |
Coefficient |
SE |
Coefficient |
SE |
Coefficient |
SE |
A B |
-1.083 |
0.174 |
-0.877 |
0.620 |
-0.206 |
0.636 |
0.746 |
A C |
-1.388 |
0.247 |
-1.869 |
0.493 |
0.481 |
0.542 |
0.375 |
A D |
-1.378 |
0.265 |
-0.738 |
0.413 |
-0.640 |
0.479 |
0.182 |
A E |
-3.425 |
0.940 |
-3.221 |
1.005 |
-0.204 |
0.937 |
0.828 |
B C |
-0.894 |
0.655 |
-0.312 |
0.297 |
-0.581 |
0.715 |
0.416 |
B D |
0.099 |
0.462 |
-0.241 |
0.329 |
0.340 |
0.567 |
0.548 |
B E |
-2.152 |
0.881 |
-2.615 |
1.087 |
0.463 |
0.896 |
0.605 |
C D |
0.490 |
0.492 |
0.177 |
0.350 |
0.313 |
0.604 |
0.605 |
D E |
-2.550 |
1.254 |
-1.956 |
0.958 |
-0.595 |
1.314 |
0.651 |
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Appendix
Appendix 1.
The extracted information from the selected articles for conducting a network meta-analysis using Stata
epih-39-e2017047-app1.pdf
Citations
Citations to this article as recorded by
- The effectiveness of parenteral agents for pain reduction in patients with migraine presenting to emergency settings: A systematic review and network analysis
Scott W. Kirkland, Lloyd Visser, Jillian Meyer, Daniela R. Junqueira, Sandra Campbell, Cristina Villa‐Roel, Benjamin W. Friedman, Nana Owusu Essel, Brian H. Rowe
Headache: The Journal of Head and Face Pain.2024; 64(4): 424. CrossRef - Comparative Muscle Tolerability of Different Types and Intensities of Statins: A Network Meta-Analysis of Double-Blind Randomized Controlled Trials
Qingtao Hou, Yuqin Chen, Yingxiao Zhang, Caishuang Pang
Cardiovascular Drugs and Therapy.2024; 38(3): 459. CrossRef - Efficacy and safety of different regimens of neoadjuvant therapy in patients with hormone receptor-positive, her2-negative breast cancer: a network meta-analysis
Yongxiao Wu, Shibo Huang, Yanlin Wei, Miaoyan Huang, Chunyan Li, Weiming Liang, Tian Qin
Frontiers in Immunology.2024;[Epub] CrossRef - Efficacy and safety of curcumin therapy for knee osteoarthritis: A Bayesian network meta-analysis
Jinlong Zhao, Guihong Liang, Guanghui Zhou, Kunhao Hong, Weiyi Yang, Jun Liu, Lingfeng Zeng
Journal of Ethnopharmacology.2024; 321: 117493. CrossRef - Effects of various exercise types on visceral adipose tissue in individuals with overweight and obesity: A systematic review and network meta‐analysis of 84 randomized controlled trials
Xiaoke Chen, Hui He, Kejia Xie, Lingtao Zhang, Chunmei Cao
Obesity Reviews.2024;[Epub] CrossRef - Physical Prehabilitation in Patients who Underwent Major Abdominal Surgery: A Comprehensive Systematic Review and Component Network Meta-Analysis Using GRADE and CINeMA Approach
Claudio Ricci, Laura Alberici, Francesco Serbassi, Paolo Caraceni, Marco Domenicali, Carlo Ingaldi, Davide Giovanni Grego, Carlo Mazzucchelli, Riccardo Casadei
Annals of Surgical Oncology.2024; 31(3): 1725. CrossRef - Effect of different exercise programs on lung function in people with chronic obstructive pulmonary disease: A network meta-analysis of RCTs
Susana Priego-Jiménez, Iván Cavero-Redondo, Carlos Pascual-Morena, Irene Martínez-García, Vicente Martínez-Vizcaíno, Celia Álvarez-Bueno
Annals of Physical and Rehabilitation Medicine.2024; 67(2): 101792. CrossRef - Optimal time to initiate early oral feeding in postoperative patients with upper gastrointestinal malignancy: A network meta-analysis
Cong-hui Guo, Jiu-di Zhong, Xiao-lei Jin, Jun-e Zhang
International Journal of Nursing Studies.2024; 151: 104680. CrossRef - Efficacy and safety of six new antiseizure medications for adjunctive treatment of focal epilepsy and epileptic syndrome: A systematic review and network meta-analysis
Jingyi Tong, Tingting Ji, Ting Liu, Jiaqi Liu, Yibin Chen, Zongjun Li, Na Lu, Qifu Li
Epilepsy & Behavior.2024; 152: 109653. CrossRef - Comparative efficacy of different exercise methods to improve cardiopulmonary function in stroke patients: a network meta-analysis of randomized controlled trials
Chengshuo Wang, Yanan Xu, Linli Zhang, Weijiao Fan, Zejian Liu, Mingjin Yong, Liang Wu
Frontiers in Neurology.2024;[Epub] CrossRef - Pediatric Central Venous Access Device Lock Solutions: A Network Meta-analysis
Mari Takashima, Yukiko Ezure, Luis Furuya-Kanamori, Joshua Wolf, Mitchell Dufficy, Victoria Gibson, Julia Clark, Amanda Ullman
Pediatrics.2024;[Epub] CrossRef - Umbrella Review: Atlas of the Meta-Analytical Evidence of Early-Onset Psychosis
Gonzalo Salazar de Pablo, Victoria Rodriguez, Filippo Besana, Serena Chiara Civardi, Vincenzo Arienti, Laura Maraña Garceo, P. Andrés-Camazón, Ana Catalan, Maria Rogdaki, Chris Abbott, Marinos Kyriakopoulos, Paolo Fusar-Poli, Christoph U. Correll, Celso A
Journal of the American Academy of Child & Adolescent Psychiatry.2024; 63(7): 684. CrossRef - Effects of interventions for enhancing resilience in cancer patients: A systematic review and network meta-analysis
Xiaotong Ding, Fang Zhao, Qing Wang, Mingyue Zhu, Houming Kan, Enfeng Fu, Shuaifang Wei, Zheng Li
Clinical Psychology Review.2024; 108: 102381. CrossRef - Effects of physical exercise interventions on balance, postural stability and general mobility in Parkinson’s disease: a network meta-analysis
Patricia Lorenzo-García, Iván Cavero-Redondo, Sergio Núñez de Arenas-Arroyo, María José Guzmán-Pavón, Susana Priego-Jiménez, Celia Álvarez-Bueno
Journal of Rehabilitation Medicine.2024; 56: jrm10329. CrossRef - Efficacy and safety of biologics, multitarget therapy, and standard therapy for lupus nephritis: a systematic review and network meta-analysis
Gui-Qing Tian, Zhen-Qiong Li
Renal Failure.2024;[Epub] CrossRef - Effect of Different Local Antibiotic Regimens on Prevention of Postoperative Infection in Clean Surgical Wounds: A Systematic Review and Network Meta-analysis
Hai Bang Pan, Yan Cui, Zhi Hang Wu, Ying Meng, Tian Ming Wang, Qi Fu, Qian Chen, Quan Xin Chen, Bo Wang
Advances in Skin & Wound Care.2024; 37(4): 216. CrossRef - Effects of psychotherapy interventions on anxiety and depression in patients with gastrointestinal cancer: A systematic review and network meta-analysis
Jianwen Chen, Li Liu, Yalan Wang, Huiying Qin, Chengjiang Liu
Journal of Psychosomatic Research.2024; 179: 111609. CrossRef - Ranking Alpha Lipoic Acid and Gamma Linolenic Acid in Terms of Efficacy and Safety in the Management of Adults With Diabetic Peripheral Neuropathy: A Systematic Review and Network Meta-analysis
Mario B. Prado, Karen Joy B. Adiao
Canadian Journal of Diabetes.2024; 48(4): 233. CrossRef - Surgical management for massive rotator cuff tears: a network meta-analysis of randomized controlled trials
Hongfu Jin, Zhenhan Deng, Jianfeng Sun, Djandan Tadum Arthur Vithran, Wenfeng Xiao, Yusheng Li
Burns & Trauma.2024;[Epub] CrossRef - Comparative efficacy of different repetitive transcranial magnetic stimulation protocols for lower extremity motor function in stroke patients: a network meta-analysis
Chengshuo Wang, Qin Zhang, Linli Zhang, Dongyan Zhao, Yanan Xu, Zejian Liu, Chunli Wu, Shengzhu Wu, Mingjin Yong, Liang Wu
Frontiers in Neuroscience.2024;[Epub] CrossRef - Newer Glucose-Lowering Drugs and Risk of Gout: A Network Meta-Analysis of Randomized Outcomes Trials
Aihua Wang, Weilong Shi, Ning Zhang, Huilin Tang, Xin Feng
Clinical Therapeutics.2024;[Epub] CrossRef - First-line systemic treatment for people with extensive-stage small cell lung cancer: a systematic review and network meta-analysis
Takenori Ichimura, Hideki Sugita, Takeshi Hasegawa, Hisashi Noma, Erika Ota, Shunsuke Toyoda
Cochrane Database of Systematic Reviews.2024;[Epub] CrossRef - Comparative Efficacy of Mind–Body Exercise for Treating Chronic Non-Specific Neck Pain: A Systematic Review and Network Meta-Analysis
Qian Gao, Xinmin Li, Mengyang Pan, Jing Wang, Fangjie Yang, Pengxue Guo, Zhenfei Duan, Chunlin Ren, Yasu Zhang
Current Pain and Headache Reports.2024; 28(6): 507. CrossRef - Effect of different exercise modalities on nonalcoholic fatty liver disease: a systematic review and network meta-analysis
Yaqi Xue, Yang Peng, Litian Zhang, Yi Ba, Gang Jin, Ge Liu
Scientific Reports.2024;[Epub] CrossRef - Comparison of weight loss interventions in overweight and obese adults with knee osteoarthritis: A systematic review and network meta-analysis of randomized trials
Arashi Shahid, Aricia Jieqi Thirumaran, Robin Christensen, Venkatesha Venkatesha, Marius Henriksen, Jocelyn L. Bowden, David J. Hunter
Osteoarthritis and Cartilage.2024;[Epub] CrossRef - Efficacy and safety of treatment modalities for cesarean scar pregnancy: a systematic review and network meta-analysis
Peiying Fu, Haiying Sun, Long Zhang, Ronghua Liu
American Journal of Obstetrics & Gynecology MFM.2024; 6(8): 101328. CrossRef - Steroid versus placebo injections and wrist splints in patients with carpal tunnel syndrome: a systematic review and network meta-analysis
Ebubechi Adindu, Sina Ramtin, Ali Azarpey, David Ring, Teun Teunis
Journal of Hand Surgery (European Volume).2024;[Epub] CrossRef - The Superiority of Schroth Exercise Combined Brace Treatment for Mild-to-Moderate Adolescent Idiopathic Scoliosis: A Systematic Review and Network Meta-Analysis
Jiping Chen, Tianwen Xu, Jianfan Zhou, Bing Han, Qingxu Wu, Wei Jin, Xianliang Zhang
World Neurosurgery.2024; 186: 184. CrossRef - Network meta-analysis of the effects of different cognitive trainings on the cognitive function of patients with mild cognitive impairment
Ji-Wei Chen, Wen-Qian Du, Kun Zhu
Journal of Psychiatric Research.2024; 174: 26. CrossRef - Effects of natural extract interventions in prostate cancer: A systematic review and network meta-analysis
Haotian Huang, Jiao Qin, Zhi Wen, Yang Liu, Caixia Chen, Chongjian Wang, Hongyuan Li, Xuesong Yang
Phytomedicine.2024; 129: 155598. CrossRef - Comparison of the efficacy and tolerability of different repetitive transcranial magnetic stimulation modalities for post-stroke dysphagia: a systematic review and Bayesian network meta-analysis protocol
Qiang Chen, Mengfan Kan, Xiaoyu Jiang, Huifen Liu, Deqi Zhang, Lin Yuan, Qiling Xu, Hongyan Bi
BMJ Open.2024; 14(4): e080289. CrossRef - Comparative efficacy of different exercise types on inflammatory markers in women with overweight and obesity: A systematic review and network meta-analysis of randomized controlled trials
Liang Tan, Weihua Yan, Bo Zhang, Zijian Zhao, Mariusz Lipowski, Zbigniew Ossowski
Journal of Science and Medicine in Sport.2024; 27(7): 458. CrossRef - Arthroscopic modified Broström may improve function while anatomic reconstructions could enhance stability for chronic lateral ankle instability: A network meta-analysis
Chun-Sheng Tsai, Ming-Tung Huang, I.-Ming Jou, Po-Ting Wu, Po-Yen Ko
Foot and Ankle Surgery.2024;[Epub] CrossRef - Clinical efficacy and safety of 16 Chinese patent medicines in combination with benzodiazepines/non-benzodiazepines for the treatment of chronic insomnia in adults: A multiple-treatment meta-analysis
Ciyan Peng, Jing Chen, Sini Li, Boyin Huang, Wei Cui, Jianhe Li, Liubao Peng
Pharmacological Research - Modern Chinese Medicine.2024; 11: 100449. CrossRef - Chemokines in diabetic eye disease
Xiongfeng Pan, Xinrui Tan, Judy McDonald, Atipatsa Chiwanda Kaminga, Yuyao Chen, Feizhao Dai, Jun Qiu, Kunyan Zhao, Yunlong Peng
Diabetology & Metabolic Syndrome.2024;[Epub] CrossRef - Comparative Efficacy and Safety of Potassium-Competitive Acid Blockers vs. Proton Pump Inhibitors for Peptic Ulcer with or without Helicobacter pylori Infection: A Systematic Review and Network Meta-Analysis
Mengling Ouyang, Shupeng Zou, Qian Cheng, Xuan Shi, Yazheng Zhao, Minghui Sun
Pharmaceuticals.2024; 17(6): 698. CrossRef - Comparison of allo-SCT, auto-SCT and chemotherapy for the treatment of patients with low- or intermediate-risk acute myeloid leukemia: a network meta-analysis
Wu Ye, Xia Wu, Ruying Zhao, Xuelian Jin, Hui Li, Ying Qu, Jie Ji, Zhigang Liu
Stem Cell Research & Therapy.2024;[Epub] CrossRef - Effects of exercise interventions on quality of life in patients with breast cancer: A systematic review and network meta‐analysis
Bing Han, Peizhen Zhang, Haojie Zhao, Xiaojie Zhang, Hongyue Gao, Jiating Dong, Liqing Zeng, Peng Pi, Jiayi Pei
Psycho-Oncology.2024;[Epub] CrossRef - Comparative efficacy and adherence of telehealth cardiac rehabilitation interventions for patients with cardiovascular disease: A systematic review and network meta-analysis
Ruru Li, Miao Wang, Shuoshuo Chen, Liqing Zhang
International Journal of Nursing Studies.2024; 158: 104845. CrossRef - PCSK9 inhibitors and inclisiran with or without statin therapy on incident muscle symptoms and creatine kinase: a systematic review and network meta-analysis
Wenshu Li, Lichaoyue Sun, Sichao Yan
Frontiers in Cardiovascular Medicine.2024;[Epub] CrossRef - Compare the efficacy of antifungal agents as primary therapy for invasive aspergillosis: a network meta-analysis
Ao Liu, Liubo Xiong, Lian Wang, Han Zhuang, Xiao Gan, Mengying Zou, Xiaoming Wang
BMC Infectious Diseases.2024;[Epub] CrossRef - Comprehensive analysis of gastrointestinal side effects in COVID-19 patients undergoing combined pharmacological treatment with azithromycin and hydroxychloroquine: a systematic review and network meta-analysis
Gabriella Pacheco, André Luis Fernandes Lopes, Ana Patrícia de Oliveira, Wendson de Ribamar Machado Corrêa, Lucas Daniel Batista Lima, Marcellus Henrique Loiola Ponte de Souza, Ariel Soares Teles, Lucas Antonio Duarte Nicolau, Jand Venes Rolim Medeiros
Critical Reviews in Toxicology.2024; 54(6): 345. CrossRef - Psychotherapies for prolonged grief disorder in adults: A systematic review and network meta-analysis
Fengwei Hao, Fanghui Qiu, Zhide Liang, Pengda Li
Asian Journal of Psychiatry.2024; 99: 104133. CrossRef - Culprit-Only Revascularization, Single-Setting Complete Revascularization, and Staged Complete Revascularization in Acute Myocardial Infarction: Insights From a Mixed Treatment Comparison Meta-Analysis of Randomized Trials
Muhammad Haisum Maqsood, Jacqueline E. Tamis-Holland, Sunil V. Rao, Gregg W. Stone, Sripal Bangalore
Circulation: Cardiovascular Interventions.2024;[Epub] CrossRef - Efficacy and safety of multiple external therapies in patients with insomnia: a systematic review and network meta-analysis
Zhen Wang, Hui Xu, Zheng Wang, Hang Zhou, Lijuan Zhang, Yu Wang, Miaoxiu Li, Yunfeng Zhou
Frontiers in Neurology.2024;[Epub] CrossRef - Effects of exercise on depression and anxiety in postmenopausal women: a pairwise and network meta-analysis of randomized controlled trials
Bing Han, Yaya Duan, Peizhen Zhang, Liqing Zeng, Peng Pi, Jiping Chen, Guoli Du
BMC Public Health.2024;[Epub] CrossRef - Comparative effects of different posterior decompression techniques for lumbar spinal stenosis: a systematic review and Bayesian network meta-analysis
Kun Wu, Zhihe Yun, Jun Zhang, Tao Yu, Anyuan Dai, Yang Sun, Chen Li, Yanli Wang, Qinyi Liu
Journal of Orthopaedic Surgery and Research.2024;[Epub] CrossRef - Comparisons of infection events associated with tumor necrosis factor inhibitors in patients with inflammatory arthritis: A systematic review and network meta-analysis
Ziwei Jiang, Yue Zou, Guangyao Li, Sixuan Zhao, Wei Zhang
Frontiers in Pharmacology.2024;[Epub] CrossRef - Effects of five types of exercise on vascular function in postmenopausal women: a network meta-analysis and systematic review of 32 randomized controlled trials
Weihao Sun, Yanli Han, Song Gu
PeerJ.2024; 12: e17621. CrossRef - Impact of Quantity and Type of Dietary Protein on Cardiovascular Disease Risk Factors Using Standard and Network Meta-analyses of Randomized Controlled Trials
Yueying Yao, Vanessa Huang, Vernice Seah, Jung Eun Kim
Nutrition Reviews.2024;[Epub] CrossRef - Comparative efficacy and safety of non-pharmacological interventions as adjunctive treatment for vascular dementia: a systematic review and network meta-analysis
Yunhao Yi, Yiwei Qu, Shimeng Lv, Guangheng Zhang, Yuanhang Rong, Ming Li
Frontiers in Neurology.2024;[Epub] CrossRef - Efficacy and safety of antithrombotic therapy for preventing and treating pediatric thromboembolic disease: a systematic review
Hongjin Gao, Mingyu Chen, Youqi Huang, Huiting Liu, Yuze Lin, Min Chen
Scientific Reports.2024;[Epub] CrossRef - Endoscopic treatment of obesity: A protocol of updated systematic review with network meta-analysis of randomized controlled trials
Eun Jeong Gong, Chang Seok Bang, Gwang Ho Baik, Dong Keon Yon
PLOS ONE.2024; 19(9): e0308410. CrossRef - Compared to exercise, the effects of exercise combined with cognitive training in people with mild cognitive impairment: a network meta-analysis
Xiaoyan Zhao, Xiuxiu Huang, Baohong Zou, Shifang Zhang, Qiaoqin Wan
Aging & Mental Health.2024; : 1. CrossRef - Optimal exercise intensity for improving executive function in patients with attention deficit hyperactivity disorder: systematic review and network meta-analysis
Ji-Wei Chen, Wen-Qian Du, Kun Zhu
European Child & Adolescent Psychiatry.2024;[Epub] CrossRef - Efficacy of technology-based cognitive and exercise interventions for mild cognitive impairment: A systematic review, network meta-analysis, and meta-regression of randomized controlled trials
Yifan Ye, Ming Lei, Liangying Chen, Rui Song, Fengjiao Zhao, Lifeng Zhang
Ageing Research Reviews.2024; 100: 102438. CrossRef - Effectiveness of non-pharmacological therapies for treating post-stroke depression: A systematic review and network meta-analysis
Yunhao Yi, Weijie Zhao, Shimeng Lv, Guangheng Zhang, Yuanhang Rong, Xin Wang, Jingrong Yang, Ming Li
General Hospital Psychiatry.2024; 90: 99. CrossRef - Nonantibiotic prophylaxis for urinary tract infections: a network meta-analysis of randomized controlled trials
Zeyu Han, Xianyanling Yi, Jin Li, Dazhou Liao, Jianzhong Ai
Infection.2024;[Epub] CrossRef - Cervical-Level Regional Paraspinal Nerve Block in Cervical Spine Surgery: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bryan Gervais de Liyis, I Gusti Agung Gede Utara Hartawan, I Putu Eka Widyadharma, Tjokorda Gde Agung Senapathi, Tjokorda Gde Bagus Mahadewa
World Neurosurgery.2024;[Epub] CrossRef - Efficacy and safety of Vonoprazan-based treatment of Helicobacter pylori infection: a systematic review and network meta-analysis
Shan Huang, Bo Li, Xue-Yao Pang, Wei-Wei Gao
BMC Infectious Diseases.2024;[Epub] CrossRef - Evaluating the efficacy of intermittent fasting and exercise combinations for weight loss: A network meta‐analysis
Xiaoyan Cheng, Shunli Sun, Maolin Chen, Xinyou Zhou, Mingxin Rao, Dongjuan Guo, Jinfeng Xie, Qiang Huang, Liqiang Su
Obesity Reviews.2024;[Epub] CrossRef - Efficacy of perioperative pain management in paediatric cardiac surgery: a protocol for a network meta-analysis
Haoqi Yan, Mengxue Yan, Yujun Xiong, Yinan Li, Hongbai Wang, Yuan Jia, Su Yuan
BMJ Open.2024; 14(9): e084547. CrossRef - Efficacy and safety of vonoprazan versus proton pump inhibitors in the treatment of peptic ulcer disease: a systematic review and network meta-analysis for randomized controlled trails
Lidi Tian, Dan Xiang, Feili Yue, Runjie Li, Youping Zhou
Frontiers in Nutrition.2024;[Epub] CrossRef - Optimal dose and type of exercise to improve cognitive function in patients with mild cognitive impairment: a systematic review and network meta-analysis of RCTs
Yingying Yu, Junjie Wang, Jian Xu
Frontiers in Psychiatry.2024;[Epub] CrossRef - Understanding Network Meta‐Analysis: A Practical Introduction for Nurses
Amir Masoud Sharifnia, Erik Biros, Michelle Cleary
Journal of Advanced Nursing.2024;[Epub] CrossRef - Procedural Outcomes With Femoral, Radial, Distal Radial, and Ulnar Access for Coronary Angiography: A Network Meta-Analysis
M. Haisum Maqsood, Celina M. Yong, Sunil V. Rao, Mauricio G. Cohen, Samir Pancholy, Sripal Bangalore
Circulation: Cardiovascular Interventions.2024;[Epub] CrossRef - Nicotine replacement therapy and oral health: a network meta-analysis of adverse effects in randomized trials
Gowri Sivaramakrishnan, Kannan Sridharan
Evidence-Based Dentistry.2024;[Epub] CrossRef - The effects of hydrotherapy and cryotherapy on recovery from acute post-exercise induced muscle damage—a network meta-analysis
Ruohan Chen, Xiaopeng Ma, Xiaoman Ma, Chenmin Cui
BMC Musculoskeletal Disorders.2024;[Epub] CrossRef - Efficacy of lower limb strengthening exercises based on different muscle contraction characteristics for knee osteoarthritis: a systematic review and network meta-analysis
Xiaoqing Ding, Yi Yang, Ying Xing, Qingsong Jia, Qingguo Liu, Jie Zhang
Frontiers in Medicine.2024;[Epub] CrossRef - The effectiveness of parenteral agents to mitigate relapses after severe acute migraine headache presentations: A systematic review and network analysis
Scott Kirkland, Jillian Meyer, Lloyd Visser, Sandra Campbell, Cristina Villa‐Roel, Benjamin W. Friedman, Nana Owusu Essel, Brian H. Rowe
Headache: The Journal of Head and Face Pain.2024;[Epub] CrossRef - Optimal exercise dose and type for improving schizophrenia symptoms in adults: A systematic review and Bayesian network meta-analysis
Yong Yang, Yuan Yuan, HaoYang Zhang, XueYing Fu, Tao Wang, JunYu Wang, Chu-Chun Fang
Neuroscience & Biobehavioral Reviews.2024; 167: 105896. CrossRef - Comparative Efficacy of Acute Exercise Intervention on Anxiety in Adolescents and Young Adults: A Network Meta‐Analysis
Jianing Tian, Qingtao Kong, Hongyan Yu, Peifeng Liu, Liqun Shen, Yan Luo
Scandinavian Journal of Medicine & Science in Sports.2024;[Epub] CrossRef - Effectiveness of multiple combined lifestyle interventions in reducing blood pressure among patients with prehypertension and hypertension: a network meta-analysis
Yuvaraj Krishnamoorthy, Ramya Nagarajan, Sharan Murali
Journal of Public Health.2023; 45(2): e319. CrossRef - Effects of non‐pharmacologic prevention on delirium in critically ill patients: A network meta‐analysis
Yutaka Matsuura, Yuko Ohno, Miki Toyoshima, Takayoshi Ueno
Nursing in Critical Care.2023; 28(5): 727. CrossRef - Efficacy of Imaging Methods in the Detection and Diagnosis of Cerebrospinal Fluid Rhinorrhea
Se Hwan Hwang, Sun Won Kim, Do Hyun Kim
The Laryngoscope.2023; 133(6): 1281. CrossRef - Congenital anomaly and perinatal outcome following blastocyst‐ vs cleavage‐stage embryo transfer: systematic review and network meta‐analysis
C. Siristatidis, M. Papapanou, V. Karageorgiou, W. P. Martins, I. Bellos, D. M. Teixeira, N. Vlahos
Ultrasound in Obstetrics & Gynecology.2023; 61(1): 12. CrossRef - Association of body mass index trajectory and hypertension risk: A systematic review of cohort studies and network meta-analysis of 89,094 participants
Ling Tan, Lin-zi Long, Xiao-chang Ma, Wen-wen Yang, Fei-fei Liao, Yu-xuan Peng, Jie-ming Lu, A-ling Shen, Dong-qing An, Hua Qu, Chang-geng Fu
Frontiers in Cardiovascular Medicine.2023;[Epub] CrossRef - Residual Disease Threshold After Primary Surgical Treatment for Advanced Epithelial Ovarian Cancer, Part 1: A Systematic Review and Network Meta-Analysis
Andrew Bryant, Eugenie Johnson, Michael Grayling, Shaun Hiu, Ahmed Elattar, Ketankumar Gajjar, Dawn Craig, Luke Vale, Raj Naik
American Journal of Therapeutics.2023; 30(1): e36. CrossRef - Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID-19
Nader Salari, Shamarina Shohaimi, Aliakbar Kiaei, Amin Hosseinian-Far, Kamran Mansouri, Arash Ahmadi, Masoud Mohammadi
Biology Methods and Protocols.2023;[Epub] CrossRef - Comparative clinical-related outcomes of Chinese patent medicines for cardiac hypertrophy: A systematic review and network meta-analysis of randomized clinical trials
Tianqi Zhang, Haoyang Xu, Dong Zhen, Danni Fu, Ming Zhao, Chengxi Wei, Xue Bai
Frontiers in Pharmacology.2023;[Epub] CrossRef - Network meta-analysis of glucose-lowering drug treatment regimens with the potential risk of hypoglycemia in patients with type 2 diabetes mellitus in terms of glycemic control and severe hypoglycemia
Satoru Kodama, Kazuya Fujihara, Hajime Ishiguro, Yasuhiro Matsubayashi, Masaru Kitazawa, Midori Iwanaga, Takaho Yamada, Kiminori Kato, Yoshimi Nakagawa, Shiro Tanaka, Hitoshi Shimano, Hirohito Sone
Journal of Investigative Medicine.2023; 71(4): 400. CrossRef - The efficacy and safety of peri-tonsillar administrated agents on pain treatment after pediatric tonsillectomy: A network meta-analysis
Do Hyun Kim, Sung Won Kim, Mohammed Abdullah Basurrah, Se Hwan Hwang
International Journal of Pediatric Otorhinolaryngology.2023; 165: 111455. CrossRef - Graphical evaluation of evidence structure within a component network meta‐analysis
Hua Li, Ming‐Chieh Shih, Yu‐Kang Tu
Research Synthesis Methods.2023; 14(4): 596. CrossRef - Optimal Hemostatic Band Duration After Transradial Angiography or Intervention: Insights From a Mixed Treatment Comparison Meta-Analysis of Randomized Trials
Muhammad Haisum Maqsood, Samir Pancholy, Kristin A. Tuozzo, Nicole Moskowitz, Sunil V. Rao, Sripal Bangalore
Circulation: Cardiovascular Interventions.2023;[Epub] CrossRef - Effects of different exercise modalities on inhibiting left ventricular pathological remodeling in patients with heart failure with reduced ejection fraction: A systematic review and network meta-analysis
Tao Wang, Lin Zhang, Mengxin Cai, Zhenjun Tian
Life Sciences.2023; 319: 121511. CrossRef - Exercise sustains motor function in Parkinson's disease: Evidence from 109 randomized controlled trials on over 4,600 patients
Meiqi Zhang, Fang Li, Dongyu Wang, Xiaohong Ba, Zhan Liu
Frontiers in Aging Neuroscience.2023;[Epub] CrossRef - Similar Effect of Exercise Therapy, Nonsteroidal Anti-inflammatory Drugs, and Opioids for Knee Osteoarthritis Pain: A Systematic Review with Network Meta-analysis
Jonas Bloch Thorlund, Milena Simic, Kenneth Pihl, Dorthe Bang Berthelsen, Richard Day, Bart Koes, Carsten Bogh Juhl
Journal of Orthopaedic & Sports Physical Therapy.2023; : 1. CrossRef - Efficacy of Phrenic Nerve Block and Suprascapular Nerve Block in Amelioration of Ipsilateral Shoulder Pain after Thoracic Surgery: A Systematic Review and Network Meta-Analysis
Tanyong Pipanmekaporn, Prangmalee Leurcharusmee, Yodying Punjasawadwong, Jiraporn Khorana, Artid Samerchua, Wariya Sukhupragarn, Isaraporn Sukuam, Nutchanart Bunchungmongkol, Surasak Saokaew
Medicina.2023; 59(2): 275. CrossRef - Efficacy and Safety of Minimally Invasive Thyroid Surgery: A Network Meta‐Analysis
Do Hyun Kim, Sung Won Kim, Geun‐Jeon Kim, Mohammed A. Basurrah, Se Hwan Hwang
The Laryngoscope.2023; 133(10): 2470. CrossRef - Efficacy and safety of TCMs with anti-inflammatory effect in patients with rheumatoid arthritis: A network meta-analysis
Jinying Fang, Mingxuan Liu, Zhenghui Huang, Yucao Ma, Yiwen Wang, Xiaojia Zheng, Liu Lv, Chunpin Liu, Wei Li, Zhenghong Zhu, Huachao Zhu, Jie Hu, Yonghong Wang, Hailong Wang
Frontiers in Immunology.2023;[Epub] CrossRef - Heart rate variability in generalized anxiety disorder, major depressive disorder and panic disorder: A network meta-analysis and systematic review
Zuxing Wang, Yuanyuan Luo, Yuan Zhang, Lili Chen, Yazhu Zou, Jun Xiao, Wenjiao Min, Cui Yuan, Yu Ye, Mingmei Li, Mengtian Tu, Jiangli Hu, Zhili Zou
Journal of Affective Disorders.2023; 330: 259. CrossRef - Surgical completeness and safety of minimally invasive thyroidectomy in patients with thyroid cancer: A network meta-analysis
Yun Jin Kang, Gulnaz Stybayeva, Se Hwan Hwang
Surgery.2023; 173(6): 1381. CrossRef - Chemoprevention of colorectal cancer in general population and high-risk population: a systematic review and network meta-analysis
Ye Ma, Wen You, Yang Cao, Xuxia He, Jing Wang, Yuelun Zhang, Ji Li, Jingnan Li
Chinese Medical Journal.2023;[Epub] CrossRef - Efficacy of electrophysical agents in fibromyalgia: A systematic review and network meta-analysis
John Marwa Gikaro, Francis Manyori Bigambo, Victor Mark Minde, Elia Asanterabi Swai
Clinical Rehabilitation.2023; 37(10): 1295. CrossRef - Evaluation of dressings preventing facial medical device‐related pressure injury in medical staff during the COVID‐19 pandemic: A systematic review and network meta‐analysis
Fangfang Zhu, Honghong Su, Yue Kong, Baoling Xu, Qian Lv, Jiamin Lin, Luanxing Yi, Jieling Xie
Journal of Clinical Nursing.2023; 32(17-18): 5988. CrossRef - Efficacy of surgical methods for peri-implantitis: a systematic review and network meta-analysis
Jing Cheng, Liang Chen, Xian Tao, Xiang Qiang, Ruiying Li, Jia Ma, Dong Shi, Zijin Qiu
BMC Oral Health.2023;[Epub] CrossRef - Efficacy and safety of surgical treatment for 1–2 cm sized lower pole of renal stone: network meta-analysis of randomized control trials
Atalel Fentahun Awedew, Yakob Sheiferawe Seman, Dawit Zerihun Yalew, Yohannes Chemere Wondmeneh, Wassie Almaw Yigzaw
Urolithiasis.2023;[Epub] CrossRef - Comparative effectiveness of glycyrrhizic acid preparations aimed at improving liver function of patients with chronic hepatitis B: A network meta-analysis of 53 randomized controlled trials
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Phytomedicine.2023; 116: 154883. CrossRef - A guide to appropriately planning and conducting meta-analyses: part 3. Special considerations—the network meta-analysis
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