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Network meta-analysis compares different interventions for the same condition, by combining direct and indirect evidence derived from all eligible studies. Network metaanalysis has been increasingly used by applied scientists and it is a major research topic for methodologists. This article describes the R package netmeta, which adopts frequentist methods to fit network meta-analysis models. We provide a roadmap to perform network meta-analysis, along with an overview of the main functions of the package. We present three worked examples considering different types of outcomes and different data formats to facilitate researchers aiming to conduct network meta-analysis with netmeta.

Background: Fall risk in the elderly is a major public health issue due to the injury-related consequences and the risk of associated long-term disability. However, delivering preventive interventions in usual clinical practice still represents a challenge. Aim: To evaluate the efficacy of a multiple-component combined with a multifactorial personalized intervention in reducing fall rates in a mixed population of community-dwelling elderly compared to usual care. Design: Randomized Controlled Trial (NCT03592420, clinicalTrials.gov). Setting: Outpatients in two Italian centers. Population: 403 community-dwelling elderly at moderate-to-high fall risk, including subjects with Parkinson's Disease and stroke. Methods: After the randomization, the described interventions were administered to the intervention group (n = 203). The control group (n = 200) received usual care and recommendations to minimize fall risk factors. In addition, each participant received a fall diary, followed by 12 monthly phone calls. The primary endpoint was the total number of falls in each group over 12 months, while the secondary endpoints were other fall-related indicators recorded at one year. In addition, participants' functioning was assessed at baseline (T1) and 3-month (T3). Results: 690 falls were reported at 12 months, 48.8% in the intervention and 51.2% in the control group, with 1.66 (& PLUSMN; 3.5) and 1.77 (& PLUSMN; 3.2) mean falls per subject, respectively. Subjects with & GE; 1 fall and & GE;2 falls were, respectively, 236 (58.6%) and 148 (36.7%). No statistically significant differences were observed between groups regarding the number of falls, the falling probability, and the time to the first fall. According to the subgroup analysis, no significant differences were reported. However, a statistically significant difference was found for the Mini-BESTest (p = 0.004) and the Fullerton Advanced Balance Scale (p = 0.006) for the intervention group, with a small effect size (Cohen's d 0.26 and 0.32, respectively), at T1 and T3 evaluations. Conclusions: The intervention was ineffective in reducing the number of falls, the falling probability, and the time to the first fall at 12 months in a mixed population of community-dwelling elderly. A significant improvement for two balance indicators was recorded in the intervention group. Future studies are needed to explore different effects of the proposed interventions to reduce falls and consequences.

Additional file 1: Appendix S1. Search strategy for systematic reviews. Figure S1. Flow diagram, identification of systematic reviews. Table S1. Criteria for rating PI/ECO-similarity degree. Table S2. Transformations made to the original data extraction. Table S3. Reasons for exclusion of systematic reviews. Table S4. Characteristics of included BoE from RCTs. Table S5. Certainty of the evidence and risk of bias for BoE from RCTs. Table S6. Characteristics of BoE from cohort studies. Table S7. Risk of bias and certainty of the evidence for BoE from cohort studies. Table S8. Heat map: instruments used for the assessment of risk of bias for BoE from RCTs and cohort studies. Table S9. Ratings of PI/ECO-similarity degree for included BoE-pairs. Table S10. Effect estimates and statistical heterogeneity for meta-analyses of RCTs and cohort studies. Figure S2a. Forest plot, analysis by population similarity degree. Figure S2b. Forest plot, analysis by intervention/ exposure similarity degree. Figure S2c. Forest plot, analysis by comparator similarity degree. Figure S2d. Forest plot, analysis by outcome similarity degree. Figure S3. Forest plot, analysis by intervention-type. Figure S3a. Forest plot, analysis of invasive procedures, stratified by PI/ECO-similarity degree. Figure S3b. Forest plot, analysis of drugs as intervention, stratified by PI/ECO-similarity degree. Figure S3c. Forest plot, analysis of nutrition as intervention, stratified by PI/ECO-similarity degree. Figure S3d. Forest plot, analysis of vaccines as intervention, stratified by PI/ECO-similarity degree. Figure S3e. Forest plot, analysis of birth assistance as intervention, stratified by PI/ECO-similarity degree. Figure S4. Forest plot, analysis of nutrition as intervention: Vitamin D/ Calcium as intervention vs. other nutrition-interventions. Figure S5. Forest plot, analysis by outcome-category. Figure S6. Sensitivity analysis: one BoE-pair per systematic review. Figure S7. Sensitivity analysis by direction of cohort study summary effect estimate.

Claudia Omarini,1 Patrizia Palumbo,2 Annarita Pecchi,3 Stefano Draisci,3 Sara Balduzzi,4 Cecilia Nasso,1 Monica Barbolini,1 Chrystel Isca,1 Alessandro Bocconi,1 Luca Moscetti,1 Silvia Galetti,5 Giovanni Tazzioli,6 Pietro Torricelli,3 Stefano Cascinu,1 Federico Piacentini1 1Division of Medical Oncology, Department of Medical and Surgical Sciences for Children & Adults, University Hospital of Modena, Modena, Italy; 2Division of Clinical Nutrition and Metabolism, Department of Specialist Medicines, University Hospital of Modena, Modena, Italy; 3Department of Radiology, University Hospital of Modena, Modena, Italy; 4Statistics Unit, Department of Medical and Surgical Sciences for Children & Adults, University Hospital of Modena, Modena, Italy; 5Division of Clinical Nutrition and Metabolism, Department of Medical and Surgical Sciences for Children & Adults, University Hospital of Modena, Modena, Italy; 6Department of General Surgery and Surgical Specialities, University Hospital of Modena, Modena, ItalyCorrespondence: Claudia OmariniDivision of Medical Oncology, Department of Medical and Surgical Sciences for Children & Adults, University Hospital of Modena, Via Del Pozzo 71, Modena 41122, ItalyTel +39 059 4222845Email claudia.omarini@gmail.comBackground: Fat tissue is strongly involved in BC tumorigenesis inducing insulin resistance, chronic inflammation and hormonal changes. Computed tomography (CT) imaging instead of body mass index (BMI) gives a reliable measure of skeletal muscle mass and body fat distribution. The impact of body composition parameters (BCPs) on chemosensitivity is still debated. We examined the associations between BCPs and tumor response to neoadjuvant chemotherapy (NC) in patients treated for operable breast cancer (BC).Methods: A retrospective review of BC patients treated with NC in Modena Cancer Center between 2005 and 2017 was performed. BCPs, such as subcutaneous fat area (SFA), visceral fat area (VFA), lumbar skeletal muscle index (LSMI) and liver-to-spleen (L/S) ratio were calculated by Advance workstation (General Electric), software ADW server 3.2 or 4.7. BMI and BCPs were correlated with pathological complete response (pCR) and survival outcomes.Results: 407 patients were included in the study: 55% with BMI < 25 and 45% with BMI ≥ 25. 137 of them had pre-treatment CT scan imagines. Overweight was significantly associated with postmenopausal status and older age. Hormonal receptor positive BC was more frequent in overweight patients (p

Additional file 1. Summary of details of studies aimed at evaluating the American Joint Committee on Cancer (AJCC) 8th edition breast cancer prognostic staging.