Review of model-based meta-analysis: bibliometric analysis_Chinese Journal of Evidence-Based Medicine

Authors：

WANG Rui ¹ , HE Hua ¹ , LIU Fengqi ² , YANG Zhirong ^3,4 , XIA Lin ⁵ , LIU Dongyang ⁵ ,  LI Ge ⁶ ,  SUN Feng ²

1. School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, P. R. China;
2. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China;
3. Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China;
4. Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China;
5. Drug Clinical Trial Center, Peking University Third Hospital, Beijing 100191, P. R. China;
6. College of Public Health Science and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, P. R. China;

Corresponding author：

LI Ge, Email: ligeself@tjutcm.edu.cn; SUN Feng, Email: sunfeng@bjmu.edu.cn

Keywords：

Model-based meta-analysis; Bibliometrics; Systematic review; Bibliometrix

DOI：

10.7507/1672-2531.202305090

Video：

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Objective This study aims to conduct a bibliometric analysis of the dataset obtained from a systematic review of Model-Based Meta-Analysis (MBMA) studies to uncover research trends in MBMA. Methods We conducted a systematic search across databases including Embase, MEDLINE, Cochrane Library, Web of Science, CNKI, VIP, WanFang Data, and Sinomed. Relevant literature pertaining to MBMA was screened and included. Qualitative and quantitative analyses were performed to assess author contributions, temporal trends, national distribution, and disease prevalence. Results A total of 129 articles were included in the analysis, most published in being China (n=48), the United States (n=47), and the United Kingdom (n=7). The time span covered by these articles ranged from 2005 to 2023. A total of 531 researchers contributed to the included studies, with first authors from 15 different countries. The compilation encompassed 402 keywords, with the three most frequently used being “Meta-analysis” (n=28), “Model-based meta-analysis” (n=27), and “Pharmacokinetics” (n=14). The study covered 16 distinct disease categories, with the top three neoplasms (n=16), neurological disorders (n=14), and endocrine and metabolic diseases (n=13). Conclusion Bibliometric analysis showed that the number of MBMA studies has increased significantly over the past three years, using a variety of key diseases as carriers. However, this new type of quantitative research has not yet attracted sufficient attention, and the research power is still concentrated in China and the United States; Moreover, a core group of authors has not yet been formed, it is necessary for scholars from various countries to strengthen multidisciplinary cooperation and communication to promote the production and translation of high-quality evidence.

Citation： WANG Rui, HE Hua, LIU Fengqi, YANG Zhirong, XIA Lin, LIU Dongyang, LI Ge, SUN Feng. Review of model-based meta-analysis: bibliometric analysis. Chinese Journal of Evidence-Based Medicine, 2023, 23(11): 1290-1298. doi: 10.7507/1672-2531.202305090 Copy

1.	DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econo, 2003, 22(2): 151-185.
2.	Ravva P, Karlsson MO, French JL. A linearization approach for the model-based analysis of combined aggregate and individual patient data. Stat Med, 2014, 33(9): 1460-1476.
3.	Brodniewicz T, Grynkiewicz G. Preclinical drug development. Acta Pol Pharm, 2010, 67(6): 578-585.
4.	Trame MN, Riggs M, Biliouris K, et al. Perspective on the state of pharmacometrics and systems pharmacology integration. CPT Pharmacometrics Syst Pharmacol, 2018, 7(10): 617-620.
5.	Elmeliegy M, Ghobrial O. Model-informed drug development and discovery: an overview of current practices (Chapter 14). Adejare A. Remington (Twenty-third Edition). Academic Press, 2021: 263-280.
6.	Chan P, Peskov K, Song X. Applications of model-based meta-analysis in drug development. Pharm Res, 2022, 39(8): 1761-1777.
7.	Ahn JE, French JL. Longitudinal aggregate data model-based meta-analysis with NONMEM: approaches to handling within treatment arm correlation. J Pharmacokinet Pharmacodyn, 2010, 37(2): 179-201.
8.	李禄金, 丁俊杰, 刘东阳, 等. 基于模型的荟萃分析一般考虑. 中国临床药理学与治疗学, 2020, 25(11): 1250-1267.
9.	Upreti VV, Venkatakrishnan K. Model-based meta-analysis: optimizing research, development, and utilization of therapeutics using the totality of evidence. Clin Pharmacol Ther, 2019, 106(5): 981-992.
10.	Boucher M, Bennetts M. The many flavors of model-based meta-analysis: part I-introduction and landmark data. CPT Pharmacometrics Syst Pharmacol, 2016, 5(2): 54-64.
11.	Deng Z, Wang H, Chen Z, et al. Bibliometric analysis of dendritic epidermal T cell (DETC) research from 1983 to 2019. Front Immunol, 2020, 11: 259.
12.	Gauthier É. Bibliometric analysis of scientific and technological research: a user's guide to the methodology - ARCHIVED. Science and Technology Redesign Project, Statistics Canada, 1998.
13.	Ejaz H, Zeeshan HM, Ahmad F, et al. Bibliometric analysis of publications on the omicron variant from 2020 to 2022 in the SCOPUS database using R and VOSviewer. Int J Environ Res Public Health, 2022, 19(19): 12407.
14.	Aria M, Cuccurullo C. Bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr, 2017, 11(4): 959-975.
15.	Ekundayo TC, Okoh AI. A global bibliometric analysis of plesiomonas-related research (1990 - 2017). PLoS One, 2018, 13(11): e207655.
16.	Chiu W, Ho Y. Bibliometric analysis of tsunami research. Scientometrics, 2007, 73(1): 3-17.
17.	Liu X, Zhan FB, Hong S, et al. A bibliometric study of earthquake research: 1900–2010. Scientometrics, 2012, 92(3): 747-765.
18.	Ji Q, Pang X, Zhao X. A bibliometric analysis of research on Antarctica during 1993–2012. Scientometrics, 2014, 101(3): 1925-1939.
19.	Nicholls PT. Price's square root law: Empirical validity and relation to Lotka's law. Infor Proces & Manage, 1988, 24(4): 469-477.
20.	张勤, 刘宇静, 左婵媛, 等. 基于文献计量学分析的全球食品追溯研究进展. 食品科学, 2023: 1-14.
21.	Mandema JW, Cox E, Alderman J. Therapeutic benefit of eletriptan compared to sumatriptan for the acute relief of migraine pain - results of a model-based meta-analysis that accounts for encapsulation. Cephalalgia, 2005, 25(9): 715-725.
22.	Zhang S, Ren YP, Shang DW, et al. Model-based meta-analysis of population pharmacokinetic models for paclitaxel in humans, rats and mice. J of Chin Pharm Sci, 2013, 48(7): 546-552.
23.	Wang Y, Sung C, Dartois C, et al. Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development. Clin Pharmacol Ther, 2009, 86(2): 167-174.
24.	Cai T, Abel L, Langford O, et al. Associations between statins and adverse events in primary prevention of cardiovascular disease: systematic review with pairwise, network, and dose-response meta-analyses. BMJ, 2021, 374: n1537.
25.	Ito K, Ahadieh S, Corrigan B, et al. Disease progression meta-analysis model in Alzheimer's disease. Alzheimers Dement, 2010, 6(1): 39-53.
26.	Rogers JA, Polhamus D, Gillespie WR, et al. Combining patient-level and summary-level data for Alzheimer's disease modeling and simulation: a β regression meta-analysis. J Pharmacokinet Pharmacodyn, 2012, 39(5): 479-498.
27.	Mandema JW, Hermann D, Wang W, et al. Model-based development of gemcabene, a new lipid-altering agent. AAPS J, 2005, 7(3): E513-E522.
28.	Mandema JW, Salinger DH, Baumgartner SW, et al. A dose-response meta-analysis for quantifying relative efficacy of biologics in rheumatoid arthritis. Clin Pharmacol Ther, 2011, 90(6): 828-835.
29.	Maloney A, Rosenstock J, Fonseca V. A model-based meta-analysis of 24 antihyperglycemic drugs for type 2 diabetes: comparison of treatment effects at therapeutic doses. Clin Pharmacol Ther, 2019, 105(5): 1213-1223.
30.	Demin I, Hamrén B, Luttringer O, et al. Longitudinal model-based meta-analysis in rheumatoid arthritis: an application toward model-based drug development. Clin Pharmacol Ther, 2012, 92(3): 352-359.
31.	Mawdsley D, Bennetts M, Dias S, et al. Model-based network meta-analysis: a framework for evidence synthesis of clinical trial data. CPT Pharmacometrics Syst Pharmacol, 2016, 5(8): 393-401.
32.	van der Wouden F, Youn H. The impact of geographical distance on learning through collaboration. Res Policy, 2023, 52(2): 104698.
33.	Flower DR. To affinity and beyond: a personal reflection on the design and discovery of drugs. Molecules, 2022, 27(21): 7624.
34.	Dimasi JA. New drug development in the United States from 1963 to 1999. Clin Pharmacol Ther, 2001, 69(5): 286-296.
35.	DiMasi JA, Bryant NR, Lasagna L. New drug development in the United States from 1963 to 1990. Clin Pharmacol Ther, 1991, 50(5 Pt 1): 471-486.
36.	Zheng X, He Y, Xu L, et al. Quantitative analysis of the placebo response in pharmacotherapy of insomnia and its application in clinical trials. Sleep, 2020, 43(5): zsz286.
37.	Zhang N, Zheng X, Liu H, et al. Testing whether the progression of Alzheimer's disease changes with the year of publication, additional design, and geographical area: a modeling analysis of literature aggregate data. Alzheimers Res Ther, 2020, 12(1): 64.
38.	Wang ZZ, Zheng QS, Liu HX, et al. Development and application of the placebo response model in clinical trials for primary sjögren's syndrome. Front Immunol, 2021, 12: 783246.
39.	Guinney J, Wang T, Laajala TD, et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol, 2017, 18(1): 132-142.
40.	韦春香, 何华, 柳晓泉. 基于模型的meta分析在药物研发中的应用. 药学进展, 2018, 42(03): 187-192.
41.	Association AS. MBMA Sub Group.
42.	Li M, Dave N, Salem AH, et al. Model-based meta-analysis of progression-free survival in non-Hodgkin lymphoma patients. Medicine (Baltimore), 2017, 96(35): e7988.
43.	Wattmo C. Prediction models for assessing long-term outcome in Alzheimer's disease: a review. Am J Alzheimers Dis Other Demen, 2013, 28(5): 440-449.
44.	Maringwa J, Sardu ML, Hang Y, et al. Characterizing effects of antidiabetic drugs on heart rate, systolic and diastolic blood pressure. Clin Pharmacol Ther, 2021, 109(6): 1583-1592.
45.	Bougioukas KI, Vounzoulaki E, Mantsiou CD, et al. Global mapping of overviews of systematic reviews in healthcare published between 2000 and 2020: a bibliometric analysis. J Clin Epid, 2021, 137: 58-72.
46.	Wouters OJ, Shadlen KC, Salcher-Konrad M, et al. Challenges in ensuring global access to COVID-19 vaccines: production, affordability, allocation, and deployment. Lancet, 2021, 397(10278): 1023-1034.
47.	Luke CJ, Subbarao K. Vaccines for pandemic influenza. Emerg Infect Dis, 2006, 12(1): 66-72.
48.	Kandala B, Plock N, Chawla A, et al. Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach. EBioMedicine, 2022, 84: 104264.
49.	Hutchinson L, Steiert B, Soubret A, et al. Models and machines: how deep learning will take clinical pharmacology to the next level. CPT Pharmacometrics Syst Pharmacol, 2019, 8(3): 131-134.
50.	Pedder H, Dias S, Bennetts M, et al. Modelling time-course relationships with multiple treatments: model-based network meta-analysis for continuous summary outcomes. Res Synth Methods, 2019, 10(2): 267-286.
51.	Pedder H, Boucher M, Dias S, et al. Performance of model-based network meta-analysis (MBNMA) of time-course relationships: a simulation study. Res Synth Methods, 2020, 11(5): 678-697.
52.	Slejko JF, Willke RJ, Ribbing J, et al. Translating pharmacometrics to a pharmacoeconomic model of COPD. Value Health, 2016, 19(8): 1026-1032.

1. DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econo, 2003, 22(2): 151-185.
2. Ravva P, Karlsson MO, French JL. A linearization approach for the model-based analysis of combined aggregate and individual patient data. Stat Med, 2014, 33(9): 1460-1476.
3. Brodniewicz T, Grynkiewicz G. Preclinical drug development. Acta Pol Pharm, 2010, 67(6): 578-585.
4. Trame MN, Riggs M, Biliouris K, et al. Perspective on the state of pharmacometrics and systems pharmacology integration. CPT Pharmacometrics Syst Pharmacol, 2018, 7(10): 617-620.
5. Elmeliegy M, Ghobrial O. Model-informed drug development and discovery: an overview of current practices (Chapter 14). Adejare A. Remington (Twenty-third Edition). Academic Press, 2021: 263-280.
6. Chan P, Peskov K, Song X. Applications of model-based meta-analysis in drug development. Pharm Res, 2022, 39(8): 1761-1777.
7. Ahn JE, French JL. Longitudinal aggregate data model-based meta-analysis with NONMEM: approaches to handling within treatment arm correlation. J Pharmacokinet Pharmacodyn, 2010, 37(2): 179-201.
8. 李禄金, 丁俊杰, 刘东阳, 等. 基于模型的荟萃分析一般考虑. 中国临床药理学与治疗学, 2020, 25(11): 1250-1267.
9. Upreti VV, Venkatakrishnan K. Model-based meta-analysis: optimizing research, development, and utilization of therapeutics using the totality of evidence. Clin Pharmacol Ther, 2019, 106(5): 981-992.
10. Boucher M, Bennetts M. The many flavors of model-based meta-analysis: part I-introduction and landmark data. CPT Pharmacometrics Syst Pharmacol, 2016, 5(2): 54-64.
11. Deng Z, Wang H, Chen Z, et al. Bibliometric analysis of dendritic epidermal T cell (DETC) research from 1983 to 2019. Front Immunol, 2020, 11: 259.
12. Gauthier É. Bibliometric analysis of scientific and technological research: a user's guide to the methodology - ARCHIVED. Science and Technology Redesign Project, Statistics Canada, 1998.
13. Ejaz H, Zeeshan HM, Ahmad F, et al. Bibliometric analysis of publications on the omicron variant from 2020 to 2022 in the SCOPUS database using R and VOSviewer. Int J Environ Res Public Health, 2022, 19(19): 12407.
14. Aria M, Cuccurullo C. Bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr, 2017, 11(4): 959-975.
15. Ekundayo TC, Okoh AI. A global bibliometric analysis of plesiomonas-related research (1990 - 2017). PLoS One, 2018, 13(11): e207655.
16. Chiu W, Ho Y. Bibliometric analysis of tsunami research. Scientometrics, 2007, 73(1): 3-17.
17. Liu X, Zhan FB, Hong S, et al. A bibliometric study of earthquake research: 1900–2010. Scientometrics, 2012, 92(3): 747-765.
18. Ji Q, Pang X, Zhao X. A bibliometric analysis of research on Antarctica during 1993–2012. Scientometrics, 2014, 101(3): 1925-1939.
19. Nicholls PT. Price's square root law: Empirical validity and relation to Lotka's law. Infor Proces & Manage, 1988, 24(4): 469-477.
20. 张勤, 刘宇静, 左婵媛, 等. 基于文献计量学分析的全球食品追溯研究进展. 食品科学, 2023: 1-14.
21. Mandema JW, Cox E, Alderman J. Therapeutic benefit of eletriptan compared to sumatriptan for the acute relief of migraine pain - results of a model-based meta-analysis that accounts for encapsulation. Cephalalgia, 2005, 25(9): 715-725.
22. Zhang S, Ren YP, Shang DW, et al. Model-based meta-analysis of population pharmacokinetic models for paclitaxel in humans, rats and mice. J of Chin Pharm Sci, 2013, 48(7): 546-552.
23. Wang Y, Sung C, Dartois C, et al. Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development. Clin Pharmacol Ther, 2009, 86(2): 167-174.
24. Cai T, Abel L, Langford O, et al. Associations between statins and adverse events in primary prevention of cardiovascular disease: systematic review with pairwise, network, and dose-response meta-analyses. BMJ, 2021, 374: n1537.
25. Ito K, Ahadieh S, Corrigan B, et al. Disease progression meta-analysis model in Alzheimer's disease. Alzheimers Dement, 2010, 6(1): 39-53.
26. Rogers JA, Polhamus D, Gillespie WR, et al. Combining patient-level and summary-level data for Alzheimer's disease modeling and simulation: a β regression meta-analysis. J Pharmacokinet Pharmacodyn, 2012, 39(5): 479-498.
27. Mandema JW, Hermann D, Wang W, et al. Model-based development of gemcabene, a new lipid-altering agent. AAPS J, 2005, 7(3): E513-E522.
28. Mandema JW, Salinger DH, Baumgartner SW, et al. A dose-response meta-analysis for quantifying relative efficacy of biologics in rheumatoid arthritis. Clin Pharmacol Ther, 2011, 90(6): 828-835.
29. Maloney A, Rosenstock J, Fonseca V. A model-based meta-analysis of 24 antihyperglycemic drugs for type 2 diabetes: comparison of treatment effects at therapeutic doses. Clin Pharmacol Ther, 2019, 105(5): 1213-1223.
30. Demin I, Hamrén B, Luttringer O, et al. Longitudinal model-based meta-analysis in rheumatoid arthritis: an application toward model-based drug development. Clin Pharmacol Ther, 2012, 92(3): 352-359.
31. Mawdsley D, Bennetts M, Dias S, et al. Model-based network meta-analysis: a framework for evidence synthesis of clinical trial data. CPT Pharmacometrics Syst Pharmacol, 2016, 5(8): 393-401.
32. van der Wouden F, Youn H. The impact of geographical distance on learning through collaboration. Res Policy, 2023, 52(2): 104698.
33. Flower DR. To affinity and beyond: a personal reflection on the design and discovery of drugs. Molecules, 2022, 27(21): 7624.
34. Dimasi JA. New drug development in the United States from 1963 to 1999. Clin Pharmacol Ther, 2001, 69(5): 286-296.
35. DiMasi JA, Bryant NR, Lasagna L. New drug development in the United States from 1963 to 1990. Clin Pharmacol Ther, 1991, 50(5 Pt 1): 471-486.
36. Zheng X, He Y, Xu L, et al. Quantitative analysis of the placebo response in pharmacotherapy of insomnia and its application in clinical trials. Sleep, 2020, 43(5): zsz286.
37. Zhang N, Zheng X, Liu H, et al. Testing whether the progression of Alzheimer's disease changes with the year of publication, additional design, and geographical area: a modeling analysis of literature aggregate data. Alzheimers Res Ther, 2020, 12(1): 64.
38. Wang ZZ, Zheng QS, Liu HX, et al. Development and application of the placebo response model in clinical trials for primary sjögren's syndrome. Front Immunol, 2021, 12: 783246.
39. Guinney J, Wang T, Laajala TD, et al. Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncol, 2017, 18(1): 132-142.
40. 韦春香, 何华, 柳晓泉. 基于模型的meta分析在药物研发中的应用. 药学进展, 2018, 42(03): 187-192.
41. Association AS. MBMA Sub Group.
42. Li M, Dave N, Salem AH, et al. Model-based meta-analysis of progression-free survival in non-Hodgkin lymphoma patients. Medicine (Baltimore), 2017, 96(35): e7988.
43. Wattmo C. Prediction models for assessing long-term outcome in Alzheimer's disease: a review. Am J Alzheimers Dis Other Demen, 2013, 28(5): 440-449.
44. Maringwa J, Sardu ML, Hang Y, et al. Characterizing effects of antidiabetic drugs on heart rate, systolic and diastolic blood pressure. Clin Pharmacol Ther, 2021, 109(6): 1583-1592.
45. Bougioukas KI, Vounzoulaki E, Mantsiou CD, et al. Global mapping of overviews of systematic reviews in healthcare published between 2000 and 2020: a bibliometric analysis. J Clin Epid, 2021, 137: 58-72.
46. Wouters OJ, Shadlen KC, Salcher-Konrad M, et al. Challenges in ensuring global access to COVID-19 vaccines: production, affordability, allocation, and deployment. Lancet, 2021, 397(10278): 1023-1034.
47. Luke CJ, Subbarao K. Vaccines for pandemic influenza. Emerg Infect Dis, 2006, 12(1): 66-72.
48. Kandala B, Plock N, Chawla A, et al. Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach. EBioMedicine, 2022, 84: 104264.
49. Hutchinson L, Steiert B, Soubret A, et al. Models and machines: how deep learning will take clinical pharmacology to the next level. CPT Pharmacometrics Syst Pharmacol, 2019, 8(3): 131-134.
50. Pedder H, Dias S, Bennetts M, et al. Modelling time-course relationships with multiple treatments: model-based network meta-analysis for continuous summary outcomes. Res Synth Methods, 2019, 10(2): 267-286.
51. Pedder H, Boucher M, Dias S, et al. Performance of model-based network meta-analysis (MBNMA) of time-course relationships: a simulation study. Res Synth Methods, 2020, 11(5): 678-697.
52. Slejko JF, Willke RJ, Ribbing J, et al. Translating pharmacometrics to a pharmacoeconomic model of COPD. Value Health, 2016, 19(8): 1026-1032.

Chinese Journal of Evidence-Based Medicine

Review of model-based meta-analysis: bibliometric analysis

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