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Sandau N, Aagaard TV, Hróbjartsson A, Harris IA, Brorson S. Transitivity, coherence, and reliability of network meta-analyses comparing proximal humerus fracture treatments: a meta-epidemiological study. BMC Musculoskelet Disord 2024; 25:14. [PMID: 38166880 PMCID: PMC10759380 DOI: 10.1186/s12891-023-07119-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 12/14/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Network meta-analyses can be valuable for decision-makers in guiding clinical practice. However, for network meta-analysis results to be reliable, the assumptions of both transitivity and coherence must be met, and the methodology should adhere to current best practices. We aimed to assess whether network meta-analyses of randomized controlled trials (RCTs) comparing interventions for proximal humerus fractures provide reliable estimates of intervention effects. METHODS We searched PubMed, EMBASE, The Cochrane Library, and Web of Science for network meta-analyses comparing interventions for proximal humerus fractures. We critically assessed the methodology regarding the development of a protocol, search strategy, trial inclusion, outcome extraction, and the methods used to conduct the network meta-analyses. We assessed the transitivity and coherence of the network graphs for the Constant score (CS), Disabilities of the Arm, Shoulder, and Hand score (DASH), and additional surgery. Transitivity was assessed by comparing probable effect modifiers (age, gender, fracture morphology, and comorbidities) across intervention comparisons. Coherence was assessed using Separating Indirect from Direct Evidence (SIDE) (Separating Indirect from Direct Evidence) and the design-by-treatment interaction test. We used CINeMA (Confidence in Network Meta-analyses) to assess the confidence in the results. RESULTS None of the three included network meta-analyses had a publicly available protocol or data-analysis plan, and they all had methodological flaws that could threaten the validity of their results. Although we did not detect incoherence for most comparisons, the transitivity assumption was violated for CS, DASH, and additional surgery in all three network meta-analyses. Additionally, the confidence in the results was 'very low' primarily due to within-study bias, reporting bias, intransitivity, imprecision, and heterogeneity. CONCLUSIONS Current network meta-analyses of RCTs comparing interventions for proximal humerus fractures do not provide reliable estimates of intervention effects. We advise caution in using these network meta-analyses to guide clinical practice. To improve the utility of network meta-analyses to guide clinical practice, journal editors should require that network meta-analyses are done according to a predefined analysis plan in a publicly available protocol and that both coherence and transitivity have been adequately assessed and reported.
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Affiliation(s)
- Nicolai Sandau
- Centre for Evidence-Based Orthopedics, Department of Orthopedic Surgery, Zealand University Hospital, Køge, Denmark.
| | - Thomas Vedste Aagaard
- The Research and Implementation Unit PROgrez, Department of Physiotherapy and Occupational Therapy, Naestved-Slagelse-Ringsted Hospitals, Naestved, Denmark
- The Department of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Asbjørn Hróbjartsson
- Centre for Evidence-Based Medicine Odense (CEBMO), and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Open Patient data Explorative Network (OPEN), Odense University Hospital, Odense, Denmark
| | - Ian A Harris
- Whitlam Orthopaedic Research Centre, Ingham Institute for Applied Medical Research, South Western Sydney Clinical School, University of New South Wales (UNSW Sydney), Liverpool, NSW, Australia
| | - Stig Brorson
- Centre for Evidence-Based Orthopedics, Department of Orthopedic Surgery, Zealand University Hospital, Køge, Denmark
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Liu M, Gao Y, Yang K, Cai Y, Xu J, Dai D, Wu S, Zhang J, Tian J. Reporting quality and risk of bias of Cochrane individual participant data meta-analyses: A cross-sectional study. J Evid Based Med 2023. [PMID: 37020358 DOI: 10.1111/jebm.12521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 02/28/2023] [Indexed: 04/07/2023]
Abstract
OBJECTIVES This study aimed to assess the reporting quality and risk of bias of Cochrane individual participant data meta-analyses (IPD-MAs). METHODS We searched the Cochrane Library and identified the Cochrane IPD-MAs. We used the Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data (PRISMA-IPD) assessed the reporting quality of included Cochrane IPD- MAs, and the Risk Of Bias In Systematic reviews (ROBIS) tool was used to assess the risk of bias. We performed stratified and correlation analyses to explore factors affecting the quality. RESULTS Forty-six Cochrane IPD-MAs were included in our study. Twenty-six Cochrane IPD-MAs (56.5%) had statistical or epidemiological authors involved, and 31 (67.4%) contained only IPD data. Thirty-five studies (76.1%) did not report whether they used 1-stage or 2-stage methods, and forty (87.0%) did not report the statistical techniques used for missing participant data. We found that the entire compliance reported PRISMA-IPD items of Cochrane IPD-MAs published after 2015 (n = 18; Mean ± SD: 26.61 ± 2.75) was higher than those studies published in 2015 and before (n = 28; Mean ± SD: 22.61 ± 4.73), the difference was statistically significant (p = 0.002). A strong positive correlation was found between the fully reported PRISMA-IPD items and fully accordance ROBIS items (Spearman's: ρ = 0.653, p < 0.001). CONCLUSIONS The quality of Cochrane IPD-MAs is not high, especially in the reporting of statistical methods. There was room for further improvement in IPD retrieval, IPD integrity and statistical analyses.
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Affiliation(s)
- Ming Liu
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Kelu Yang
- Department of Public Health and Primary Care, Academic Centre for Nursing and Midwifery, KU Leuven-University of Leuven, Leuven, Belgium
| | - Yitong Cai
- Nursing Psychology Research Center, Xiangya School of Nursing, Central South University, Changsha, China
| | - Jianguo Xu
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Dingmei Dai
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Shuilin Wu
- School of Public Health, Lanzhou University, Lanzhou, China
| | - Junhua Zhang
- Evidence-Based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
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Tian J, Gao Y, Zhang J, Yang Z, Dong S, Zhang T, Sun F, Wu S, Wu J, Wang J, Yao L, Ge L, Li L, Shi C, Wang Q, Li J, Zhao Y, Xiao Y, Yang F, Fan J, Bao S, Song F. Progress and challenges of network meta-analysis. J Evid Based Med 2021; 14:218-231. [PMID: 34463038 DOI: 10.1111/jebm.12443] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/03/2021] [Accepted: 08/03/2021] [Indexed: 11/28/2022]
Abstract
In the past years, network meta-analysis (NMA) has been widely used among clinicians, guideline makers, and health technology assessment agencies and has played an important role in clinical decision-making and guideline development. To inform further development of NMAs, we conducted a bibliometric analysis to assess the current status of published NMA methodological studies, summarized the methodological progress of seven types of NMAs, and discussed the current challenges of NMAs.
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Affiliation(s)
- Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
| | - Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
| | - Junhua Zhang
- Evidence-Based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Zhirong Yang
- Primary Care Unit, Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Shengjie Dong
- Orthopedic Department, Yantaishan Hospital, Yantai, Shandong, China
| | - Tiansong Zhang
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital, Shanghai, China
| | - Feng Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Shanshan Wu
- National Clinical Research Center of Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jiarui Wu
- Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Junfeng Wang
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Liang Yao
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Long Ge
- Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China
- Evidence-Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou, China
| | - Lun Li
- Department of Breast Cancer, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Quan Wang
- Department of Gastrointestinal Surgery, Peking University People's Hospital, Beijing, China
| | - Jiang Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ye Zhao
- First Clinical Medical College, Lanzhou University, Lanzhou, China
- Departments of Biochemistry and Molecular Biology, Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, Indiana
| | - Yue Xiao
- China National Health Development Research Center, Beijing, China
| | - Fengwen Yang
- Evidence-Based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jinchun Fan
- Epidemiology and Evidence Based-Medicine, School of Public Health, Gansu University of Chinese Medicine, Lanzhou, China
| | - Shisan Bao
- Epidemiology and Evidence Based-Medicine, School of Public Health, Gansu University of Chinese Medicine, Lanzhou, China
- Sydney, NSW, Australia
| | - Fujian Song
- Public Health and Health Services Research, Norwich Medical School, University of East Anglia, Norwich, UK
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Gao Y, Liu M, Shi S, Niu M, Li J, Zhang J, Song F, Tian J. Prespecification of subgroup analyses and examination of treatment-subgroup interactions in cancer individual participant data meta-analyses are suboptimal. J Clin Epidemiol 2021; 138:156-167. [PMID: 34186194 DOI: 10.1016/j.jclinepi.2021.06.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 06/18/2021] [Accepted: 06/22/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVES This study aimed to explore the prespecification and conduct of subgroup analyses in cancer individual participant data meta-analyses (IPDMAs). STUDY DESIGN AND SETTING We searched PubMed, Embase.com, Cochrane Library, and Web of Science to identify IPDMAs of randomized controlled trials evaluating intervention effects for cancer. We evaluated how often cancer IPDMAs prespecify subgroup analyses and statistical approaches for examining treatment-subgroup interactions and handling continuous subgroup variables. RESULTS We included 89 IPDMAs, of which 41 (46.1%) reported a statistically significant treatment-subgroup interaction (P < 0.05) in at least one subgroup analysis. 47 (52.8%) IPDMAs prespecified methods for conducting subgroup analyses and the remaining 42 (47.2%) did not prespecify subgroup analyses. Of the 47 IPDMAs prespecified subgroup analyses, 19 performed the planned subgroup analyses, 21 added subgroup analyses, 7 reduced subgroup analyses. Eighty IPDMAs examined treatment-subgroup interactions, but 72 IPDMAs did not provide enough information to determine whether an appropriate approach that avoided aggregation bias was used. 85 IPDMAs that used continuous variables in subgroup analyses categorized continuous variables and only 1 IPDMA examined non-linear relationships. CONCLUSION Many cancer IPDMAs did not prespecify subgroup analyses, nor did they fully perform planned subgroup analyses. Lack of details for the test of treatment-subgroup interactions and examination of non-linear interactions was suboptimal.
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Affiliation(s)
- Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Ming Liu
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Shuzhen Shi
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Mingming Niu
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Jiang Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking UnionMedical College, Beijing, China
| | - Junhua Zhang
- Evidence-Based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Fujian Song
- Public Health and Health Services Research, Norwich Medical School, University of East Anglia, Norwich, UK.
| | - Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China; Key Laboratory of Evidence-Based Medicine and Knowledge Translation of Gansu Province, Lanzhou, China.
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Chaimani A. Conduct and reporting of individual participant data network meta-analyses need improvement. BMC Med 2020; 18:156. [PMID: 32482163 PMCID: PMC7265632 DOI: 10.1186/s12916-020-01630-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 05/13/2020] [Indexed: 01/08/2023] Open
Affiliation(s)
- Anna Chaimani
- Université de Paris, Research Center of Epidemiology and Statistics (CRESS-U1153), INSERM, INRA, F-75004, Paris, France.
- Cochrane France, Paris, France.
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