Performing network meta-analysis using cross-design evidence and cross-format data in crossnma package of R software_Chinese Journal of Evidence-Based Medicine

Authors：

LIU Runben , ZHANG Zhixin , HUANG Chengyang , LI Haoyang , ZHAO Yifan , ZHANG Chao ,  LUO Jie

Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, P. R. China;

Corresponding author：

LUO Jie, Email: taihehospital@yeah.net

Keywords：

Network meta-analysis; Cross-design evidence and cross-format data; Individual participant data; Aggregate data; Crossnma package; R programming language

DOI：

10.7507/1672-2531.202303092

Video：

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Abstract Full text Figures/Tables Video References Cited by

Network meta-analysis (NMA) is a statistical technique that integrates data from multiple clinical studies and compares the efficacy and safety of multiple interventions, which can provide pro and con ranking results for all intervention options in the evidence network and provide direct evidence support for clinical decision-making. At present, NMA is usually based on the aggregation of the same type of data set, and there are still methodological and software difficulties in achieving cross-study design and cross-data format data set merging. The crossnma package of R programming language is based on Bayesian framework and Markov chain Monte Carlo algorithm, extending the three-level hierarchical model to the standard NMA data model to achieve differential merging of varied data types. The crossnma package fully considers the impact of risk bias caused by the combination of different types of data on the results by introducing model variables. In addition, the package provides functions such as result output and easy graphing, which makes it possible to combine NMA across study designs and evidence across data formats. In this study, the model based on crossnma package method and software operation will be demonstrated and explained through the examples of four individual participant datasets and two aggregate datasets.

Citation： LIU Runben, ZHANG Zhixin, HUANG Chengyang, LI Haoyang, ZHAO Yifan, ZHANG Chao, LUO Jie. Performing network meta-analysis using cross-design evidence and cross-format data in crossnma package of R software. Chinese Journal of Evidence-Based Medicine, 2023, 23(10): 1197-1203. doi: 10.7507/1672-2531.202303092 Copy

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1. Anglemyer A, Horvath HT, Bero L. Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials. Cochrane Database Syst Rev, 2014, (4): MR000034.
2. Schulz KF, Chalmers I, Hayes RJ, et al. Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA, 1995, 273(5): 408-412.
3. Chalmers TC, Celano P, Sacks HS, et al. Bias in treatment assignment in controlled clinical trials. N Engl J Med, 1983, 309(22): 1358-1361.
4. Wood L, Egger M, Gluud LL, et al. Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study. BMJ, 2008, 336(7644): 601-605.
5. Signorovitch JE, Wu EQ, Yu AP, et al. Comparative effectiveness without head-to-head trials: a method for matching-adjusted indirect comparisons applied to psoriasis treatment with adalimumab or etanercept. Pharmacoeconomics, 2010, 28(10): 935-945.
6. Ishak KJ, Proskorovsky I, Benedict A. Simulation and matching-based approaches for indirect comparison of treatments. Pharmacoeconomics, 2015, 33(6): 537-549.
7. Phillippo DM, Dias S, Ades AE, et al. Multilevel network meta-regression for population-adjusted treatment comparisons. J R Stat Soc Ser A Stat Soc, 2020, 183(3): 1189-1210.
8. Jansen JP. Network meta-analysis of individual and aggregate level data. Res Synth Methods, 2012, 3(2): 177-190.
9. Dias S, Welton NJ, Marinho VCC, et al. Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta-analysis. J R Statist Soc A, 2010, 173(3): 613-629.
10. Disanto G, Benkert P, Lorscheider J, et al. The Swiss Multiple Sclerosis Cohort-Study (SMSC): a prospective Swiss wide investigation of key phases in disease evolution and new treatment options. PLoS One, 2016, 11(3): e0152347.
11. Fox RJ, Miller DH, Phillips JT, et al. Placebo-controlled phase 3 study of oral BG-12 or glatiramer in multiple sclerosis. N Engl J Med, 2012, 367(12): 1087-1097.
12. Gold R, Kappos L, Arnold DL, et al. Placebo-controlled phase 3 study of oral BG-12 for relapsing multiple sclerosis. N Engl J Med, 2012, 367(12): 1098-1107.
13. Polman CH, O'Connor PW, Havrdova E, et al. A randomized, placebo-controlled trial of natalizumab for relapsing multiple sclerosis. N Engl J Med, 2006, 354(9): 899-910.
14. Bornstein MB, Miller A, Slagle S, et al. A pilot trial of Cop 1 in exacerbating-remitting multiple sclerosis. N Engl J Med, 1987, 317(7): 408-414.
15. Johnson KP, Brooks BR, Cohen JA, et al. Copolymer 1 reduces relapse rate and improves disability in relapsing-remitting multiple sclerosis: results of a phase III multicenter, double-blind, placebo-controlled trial. Neurology, 1995, 45(7): 1268-1276.
16. 董圣杰, 曾宪涛, 郭毅. R软件Metafor程序包在Meta分析中的应用. 中国循证医学杂志, 2012, 12(9): 1141-1147.
17. 张超, 孙凤, 曾宪涛. R软件调用JAGS软件实现网状Meta分析. 中国循证医学杂志, 2014, 14(2): 241-248.
18. Savovic J, Turner RM, Mawdsley D, et al. Association between risk-of-bias assessments and results of randomized trials in cochrane reviews: the ROBES meta-epidemiologic study. Am J Epidemiol, 2018, 187(5): 1113-1122.
19. Lee KJ, Thompson SG. Flexible parametric models for random-effects distributions. Stat Med, 2008, 27(3): 418-434.
20. Sarri G, Patorno E, Yuan H, et al. Framework for the synthesis of non-randomised studies and randomised controlled trials: a guidance on conducting a systematic review and meta-analysis for healthcare decision making. BMJ Evid Based Med, 2022, 27(2): 109-119.

Chinese Journal of Evidence-Based Medicine

Performing network meta-analysis using cross-design evidence and cross-format data in crossnma package of R software

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