Current status analysis of anti-infection research by using the Bayesian method

Published on Jan. 25, 2025Total Views: 156 times Total Downloads: 32 times Download Mobile

Author: LIU Yiling ^1, ² LIU Jinglin ³ LU Zhenzhen ¹ WANG Yingying ¹ JIANG Qijing ⁴ LI Bingzhe ¹ DAI Luyan ⁵  YAN Fanrong ²  HUANG Lihong ¹

Affiliation: 1. Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, China 2. School of Science, China Pharmaceutical University, Nanjing 211198, China 3. School of Economics and Management, East China Normal University, Shanghai 200062, China 4. School of Public Health, Fudan University, Shanghai 200032, China 5. Cui Yu Business Consulting Company, Shanghai 200000, China

Keywords: Bayesian method Anti-infection research Research features

DOI: 10.12173/j.issn.1005-0698.202408053

Reference: LIU Yiling, LIU Jinglin, LU Zhenzhen, WANG Yingying, JIANG Qijing, LI Bingzhe, DAI Luyan, YAN Fanrong,HUANG Lihong. Current status analysis of anti-infection research by using the Bayesian method[J]. Yaowu Liuxingbingxue Zazhi, 2025,34(1): 69-77. DOI: 10.12173/j.issn.1005-0698.202408053.[Article in Chinese]  Copied

Abstract
Full-text
References

Abstract

Objective To analyze the application characteristics, trends, and special advantages of anti-infection research using the Bayesian method, and to provide methodological reference for the development of anti-infection research.

Methods PubMed, CNKI and WanFang Data were electronically searched for the studies on anti-infection using Bayesian method published from January 1, 2015 to November 21, 2023. The relevant information of publication time, anti-infection type, sample size, Bayesian characteristics and Bayesian application pattern were analyzed descriptively and reviewed.

Results A total of 86 studies were included, of which 41.9% were observational studies, only 7.0% were enterprise-initiated studies, and 48.8% were mentioning prior information studies. There was no domestic intervention study. The application characteristics and advantages of Bayesian method in intervention study, observational study and pharmacokinetic study are different. In intervention researches and observational researches, the application of Bayesian design decision and the application of Bayesian analysis and estimation accounts for 69.2% and 52.8% at most, respectively.

Conclusions The Bayesian method is flexible, can be used for small sample sizes and complex model research, and can deal with uncertainty. In intervention studies in the field of anti-infection in China, the Bayesian method has not been applied widely. Only a handful of studies applying Bayesian method have been initiated by companies. In the future, it is still necessary to promote the advantages and application scenarios of Bayesian methods in the field of anti-infection research and strengthen the standardization of the application of Bayesian method.

Full-text

Please download the PDF version to read the full text: download

References

1.陈旭岩, 张陈光, 杨浩. 感染性疾病研究进展与展望[J]. 中国急救医学, 2024, 44(1): 13-20. [Chen XY, Zhang CG, Yang H. Research progress and prospect of infectious diseases[J]. Chinese Journal of Critical Care Medicine, 2024, 44(1): 13-20.] DOI: 10.3969/j.issn.1002-1949.2024.01.003.

2.Zumla A, Rao M, Wallis RS, et al. Host-directed therapies for infectious diseases: current status, recent progress, and future prospects[J]. Lancet Infect Dis, 2016, 16(4): e47-63. DOI: 10.1016/S1473-3099(16)00078-5.

3.Wong SY, Tan BH. Megatrends in infectious diseases: the next 10 to 15 years[J]. Ann Acad Med Singap, 2019, 48(6): 188-194. https://pubmed.ncbi.nlm.nih.gov/31377763.

4.Larsson DGJ, Flach CF. Antibiotic resistance in the environment[J]. Nat Rev Microbiol, 2022, 20(5): 257-269. DOI: 10.1038/s41579-021-00649-x.

5.茆诗松, 汤银才, 主编. 贝叶斯统计, 第2版[M]. 北京: 中国统计出版社, 2012: 12-15.

6.Wasserstein RL, Lazar NA. The ASA statement on p-values: context, process, and purpose[J]. Am Stat, 2016, 70(2): 129-133. https://doi.org/10.1080/00031305.2016.1154108.

7.Berry SM, Carlin BP, Lee JJ, et al. Bayesian adaptive methods for clinical trials[M]. Boca Raton, FL: Chapman & Hall/CRC Press, 2010.

8.曾宪涛, 包翠萍, 曹世义, 等. Meta分析系列之三:随机对照试验的质量评价工具[J]. 中国循证心血管医学杂志, 2012, 4(3): 183-185. [Zeng XT, Bao CP, Cao SY, et al. Meta-analysis series 3: quality assessment tools for randomized controlled trials[J]. Chinese Journal of Evidence-Bases Cardiovascular Medicine, 2012, 4(3): 183-185.] DOI: 10.3969/j.issn.1674-4055.2012.03.003.

9.曾宪涛, 刘慧, 陈曦, 等. Meta分析系列之四:观察性研究的质量评价工具[J]. 中国循证心血管医学杂志, 2012, 4(4): 297-299. [Zeng XT, Liu H, Chen X, et al. Meta-analysis series 4: quality assessment tools for observational studies[J]. Chinese Journal of Evidence-Bases Cardiovascular Medicine, 2012, 4(4): 297-299.] DOI: 10.3969/j.1674-4055.2012.04.004.

10.Butler CC, van der Velden AW, Bongard E, et al. Oseltamivir plus usual care versus usual care for influenza-like illness in primary care: an open-label, pragmatic, randomised controlled trial[J]. Lancet, 2020,395(10217): 42-52. DOI: 10.1016/S0140-6736(19)32982-4.

11.蔡乐, 朱曼, 郭代红, 等. 基于数据挖掘技术对抗感染药相关肝损伤信号的分析研究[J]. 药物流行病学杂志, 2016, 25(2): 89-93. [Cai L, Zhu M, Guo DH, et al. Analysis of anti-infectious agents related hepatic adverse drug reactions based on data mining methods[J]. Chinese Journal of Pharmacoepidemiology, 2016, 25(2): 89-93.] DOI: 10.19960/j.cnki.issn1005-0698.2016.02.007.

12.潘辛梅, 马攀.基于FAERS数据库对玛巴洛沙韦安全警戒信号的挖掘与分析[J]. 中国抗生素杂志, 2023, 48(4): 466-472. [Pan XM, Ma P. Detection and analysis of the safety signals of baloxavir marboxil based on FAERS database[J]. Chinese Journal of Antibiotics, 2023, 48(4): 466-472.] DOI: 10.3969/j.issn.1001-8689.2023.04.015.

13.申川, 魏梦平, 马路园, 等. 个体化抗感染治疗策略与临床实践[J]. 中国实用内科杂志, 2023, 43(8): 655-660. [Shen C, Wei MP, Ma LY, et al. Individualized anti-infective treatment strategy and clinical practice[J]. Chinese Journal of Practical Internal Medicine, 2023, 43(8): 655-660.] DOI: 10.19538/j.nk2023080108.

14.Neely MN, Kato L, Youn G, et al. Prospective trial on the use of trough concentration versus area under the curve to determine therapeutic vancomycin dosing[J]. Antimicrob Agents Chemother, 2018, 62(2): e02042-17. DOI: 10.1128/AAC.02042-17.

15.周尔君, 潘雁, 朱敏, 等.基于最大后验贝叶斯法的个体化用药研究进展[J]. 药学进展, 2020, 44(6): 411-417. [Zhou EJ, Pan Y, Zhu M, et al. Advance of research on individualized dosing based on maximum a posteriori bayesian method[J]. Progress in Pharmaceutical Sciences, 2020, 44(6): 411-417.] https://pps.cpu.edu.cn/cn/article/pdf/preview/4632bc77-2682-4593-a4ce-23d29190a4ef.

16.丁俊杰, 焦正, 王艺. 基于D最优设计的最大后验贝叶斯法估算个体药动学参数[J]. 药学学报, 2011, 46(12): 1493-1500.[Ding JJ, Jiao Z, Wang Y. Individual pharmacokinetic parameters were estimated based on maximum a posteriori Bayesian with a D-optimal design[J]. Acta Pharmaceutica Sinica B, 2011, 46(12): 1493-1500.] DOI: 10.16438/j.0513-4870.2011.12.015.

17.Donagher J, Martin JH, Barras MA. Individualised medicine: why we need Bayesian dosing[J]. Intern Med J, 2017, 47(5): 593-600.DOI: 10.1111/imj.13412.

18.FDA. Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials[EB/OL]. (2010) [2023-11-21]. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-use-bayesian-statistics-medical-device-clinical-trials.

19.Giovagnoli A. The Bayesian design of adaptive clinical trials[J]. Int J Environ Res Public Health, 2021, 18(2): 530. DOI: 10.3390/ijerph18020530.

20.Nistal-nuno B. Tutorial of the probabilistic methods Bayesian networks and influence diagrams applied to medicine[J]. J Evid Based Med, 2018, 11(2): 112-124. DOI: 10.1111/jebm.12298.

21.王莹, 尤心怡, 王奕婧, 等. 中国新型冠状病毒肺炎疫情基本再生数评估[J]. 中华流行病学杂志, 2020, 41(4): 476-479. [Wang Y, You XY, Wang YJ, et al. Assessment of basic reproduction number of COVID-19 in China[J]. Chinese Journal of Epidemiology, 2020, 41(4): 476-479.] DOI: 10.3760/cma.j.cn112338-20200210-00086.

22.何沅宸, 孙传瑞, 陈胤孜, 等. 基于实时再生数的COVID-19预测模型及可视化平台开发[J]. 复旦学报（医学版）, 2023, 50(5): 743-749, 760. [He YC, Sun CR, Chen YZ, et al. Real-time reproduction number based COVID-19 prediction model and visualization platform development[J]. Fudan University Journal of Medical Sciences, 2023, 50(5): 743-749, 760.] DOI: 10.3969/j.issn.1672-8467.2023.05.013.

23.郑杨, 李晓松. 贝叶斯时空模型在疾病时空数据分析中的应用[J]. 中华预防医学杂志, 2010, 44(12): 1136-1139. [Zheng Y, Li XS. Application of Bayesian spatio-temporal models to the analysis of spatio-temporal data of diseases[J].Chinese Journal of Preventive Medicine, 2010, 44(12): 1136-1139.] DOI: 10.3760/cma.j.issn.0253-9624.2010.12.016.

24.Pullan RL, Sturrock HJ, Soares Magalhaes RJ, et al. Spatial parasite ecology and epidemiology: a review of methods and applications[J]. Parasitology, 2012, 139(14): 1870-1887. DOI: 10.1017/S0031182012000698.

25.Writing Committee for the REMAP-CAP Investigators, Estcourt LJ, Turgeon AF, et al. Effect of convalescent plasma on organ support-free days in critically ill patients with COVID-19: a randomized clinical trial[J]. JAMA, 2021, 326(17): 1690-1702. DOI: 10.1001/jama.2021.18178.

26.Waddington CS, Mcleod C, Morris P, et al. The NICE-GUT trial protocol: a randomised, placebo controlled trial of oral nitazoxanide for the empiric treatment of acute gastroenteritis among Australian aboriginal children[J]. BMJ Open, 2018, 8(2): e019632. DOI: 10.1136/bmjopen-2017-019632.

27.Thall PF, Simon R. Practical Bayesian guidelines for phase ⅡB clinical trials[J]. Biometrics, 1994, 50(2): 337-349. https://pubmed.ncbi.nlm.nih.gov/7980801.