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Сетевой метаанализ для клиницистов
Сетевой метаанализ для клиницистов
Богданов А.А., Богданов Ан.А. Сетевой метаанализ для клиницистов. Современная Онкология. 2021; 23 (3): 418–424. DOI: 10.26442/18151434.2021.3.201202
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Аннотация
Принятие решений в клинической практике требует учета относительной эффективности и безопасности медицинских вмешательств. Систематический обзор и метаанализ, результаты которых имеют высший уровень достоверности в доказательной медицине, допускают сравнивать одновременно эффективность только двух вмешательств, при условии, что между ними проведено прямое сопоставление в наборе рандомизированных контролируемых исследований. Развитие статистических методов привело к развитию метода сетевого метаанализа, его применение позволяет проводить сравнение более чем для двух вмешательств и даже в том случае, если вмешательства не сопоставлены напрямую в рандомизированных контролируемых исследованиях, но имеют общее вмешательство сравнения. За счет этого сетевой метаанализ все чаще используется в качестве доказательной базы эффективности медицинских вмешательств. Однако есть важные допущения и условия, лежащие в основе выполнения сетевого метаанализа. В настоящей работе мы попытались изложить основные аспекты сетевого метаанализа, важные для клиницистов в части его выполнения и интерпретации результатов.
Ключевые слова: доказательная медицина, сравнительная эффективность медицинских вмешательств, сетевой метаанализ
Keywords: evidence-based medicine, comparative effectiveness of medical interventions, network meta-analysis
Ключевые слова: доказательная медицина, сравнительная эффективность медицинских вмешательств, сетевой метаанализ
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Keywords: evidence-based medicine, comparative effectiveness of medical interventions, network meta-analysis
Полный текст
Список литературы
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2. Murad MH, Asi N, Alsawas M, Alahdab F. New evidence pyramid. Evidence Based Medicine. 2016;21(4):125-7. DOI:10.1136/ebmed-2016-110401
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4. Khan KS, Kunz R, Kleijnen J, Antes G. Five steps to conducting a systematic review. J R Soc Med. 2003;96(3):118-21. DOI:10.1258/jrsm.96.3.118
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10. Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004;23(20):3105-24. DOI:10.1002/sim.1875
11. Faltinsen EG, Storebø OJ, Jakobsen JC, et al. Network meta-analysis: the highest level of medical evidence? BMJ Evid Based Med. 2018;23(2):56-9.
DOI:10.1136/bmjebm-2017-110887
12. Dias S, Caldwell DM. Network meta-analysis explained. Arch Dis Child Fetal Neonatal Ed. 2019;104(1):F8-12. doi:10.1136/archdischild-2018-315224
13. Rouse B, Chaimani A, Li T. Network meta-analysis: an introduction for clinicians. Intern Emerg Med. 2017;12(1):103-11. DOI:10.1007/s11739-016-1583-7
14. Tonin FS, Rotta I, Mendes AM, Pontarolo R. Network meta-analysis: a technique to gather evidence from direct and indirect comparisons. Pharm Pract (Granada). 2017;15(1):943. DOI:10.18549/PharmPract.2017.01.943
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16. Zarin W, Veroniki AA, Nincic V, et al. Characteristics and knowledge synthesis approach for 456 network meta-analyses: a scoping review. BMC Med. 2017;15(1):3. DOI:10.1186/s12916-016-0764-6
17. Ge L, Tian J-h, Li X-x, et al. Epidemiology Characteristics, Methodological Assessment and Reporting of Statistical Analysis of Network Meta-Analyses in the Field of Cancer. Sci Rep. 2016;6(1):37208. DOI:10.1038/srep37208
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19. Hutton B, Salanti G, Caldwell DM, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162(11):777‑84. DOI:10.7326/m14-2385
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21. Rethlefsen ML, Murad MH, Livingston EH. Engaging Medical Librarians to Improve the Quality of Review Articles. JAMA. 2014;312(10):999-1000. DOI:10.1001/jama.2014.9263
22. Li L, Tian J, Tian H, et al. Network meta-analyses could be improved by searching more sources and by involving a librarian. J Clin Epidemiol.2014;67(9):1001-7. DOI:10.1016/j.jclinepi.2014.04.003
23. Altwairgi AK, Booth CM, Hopman WM, Baetz TD. Discordance Between Conclusions Stated in the Abstract and Conclusions in the Article: Analysis of Published Randomized Controlled Trials of Systemic Therapy in Lung Cancer. J Clin Oncol. 2012;30(28):3552-7. DOI:10.1200/jco.2012.41.8319
24. McAuley L, Pham B, Tugwell P, Moher D. Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses? The Lancet. 2000;356(9237):1228-31. DOI:10.1016/S0140-6736(00)02786-0
25. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Br Med J (Clin Res Ed). 1997;315(7109):629-34.
DOI:10.1136/bmj.315.7109.629
26. Trinquart L, Ioannidis JPA, Chatellier G, Ravaud P. A test for reporting bias in trial networks: simulation and case studies. BMC Medical Research Methodology. 2014;14(1):112. DOI:10.1186/1471-2288-14-112
27. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Br Med J (Clin Res Ed). 2003;327(7414):557-60.
DOI:10.1136/bmj.327.7414.557
28. Watt J, Tricco AC, Straus S, et al. Research Techniques Made Simple: Network Meta-Analysis. J Invest Dermatol. 2019;139(1):4-12.e1. DOI:10.1016/j.jid.2018.10.028
29. Higgins J, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions version 6.2. Cochrane, 2021.
30. Cipriani A, Higgins JP, Geddes JR, Salanti G. Conceptual and technical challenges in network meta-analysis. Ann Intern Med. 2013;159(2):130-7.
DOI:10.7326/0003-4819-159-2-201307160-00008
31. IntHout J, Ioannidis JP, Borm GF. Obtaining evidence by a single well-powered trial or several modestly powered trials. Statistical Methods in Medical Research. 2016;25(2):538-52. DOI:10.1177/0962280212461098
32. Serghiou S, Goodman SN. Random-Effects Meta-analysis: Summarizing Evidence With Caveats. JAMA. 2019;321(3):301-2. DOI:10.1001/jama.2018.19684
33. Efthimiou O, Debray TP, van Valkenhoef G, et al. GetReal in network meta-analysis: a review of the methodology. Res Synth Methods. 2016;7(3):236-63. DOI:10.1002/jrsm.1195
34. Hassan S, Ravishankar N, Nair SN. Methodological considerations in network meta-analysis. Int J Med Sci Public Health. 2015;4(5):1-7.
35. Dias S, Ades AE, Welton NJ, et al. Network meta-analysis for decision-making: John Wiley and Sons, 2018.
36. Aronow PM, Miller BT. Foundations of agnostic statistics: Cambridge University Press, 2019.
37. Greco T, Edefonti V, Biondi-Zoccai G, et al. A multilevel approach to network meta-analysis within a frequentist framework. Contemporary Clinical Trials. 2015;42:51‑9. DOI:10.1016/j.cct.2015.03.005
38. Madden LV, Piepho HP, Paul PA. Statistical Models and Methods for Network Meta-Analysis. Phytopathology. 2016;106(8):792-806. DOI:10.1094/phyto-12-15-0342-rvw
39. Kim H, Gurrin L, Ademi Z, Liew D. Overview of methods for comparing the efficacies of drugs in the absence of head-to-head clinical trial data. Brit J Clin Pharm. 2014;77(1):116-21. DOI:10.1111/bcp.12150
40. Ohlssen D, Price KL, Xia HA, et al. Guidance on the implementation and reporting of a drug safety Bayesian network meta-analysis. Pharm Stat. 2014;13(1):55-70. DOI:10.1002/pst.1592
41. Kibret T, Richer D, Beyene J. Bias in identification of the best treatment in a Bayesian network meta-analysis for binary outcome: a simulation study. Clin Epidemiol. 2014;6:451-60. DOI:10.2147/clep.s69660
42. Uhlmann L, Jensen K, Kieser M. Bayesian network meta-analysis for cluster randomized trials with binary outcomes. Res Synth Methods. 2017;8(2):236-50. DOI:10.1002/jrsm.1210
43. Shim SR, Kim SJ, Lee J, Rücker G. Network meta-analysis: application and practice using R software. Epidemiol Health. 2019;41:e2019013. DOI:10.4178/epih.e2019013
44. Seide SE, Jensen K, Kieser M. A comparison of Bayesian and frequentist methods in random-effects network meta-analysis of binary data. Res Synth Methods. 2020;11(3):363-78. DOI:10.1002/jrsm.1397
45. Rücker G, Krahn U, König J, et al. Network meta-analysis using frequentist methods. (netmeta): R package version 1.2-1. 2020
46. van Valkenhoef G, Lu G, de Brock B, et al. Automating network meta-analysis. Res Synth Methods. 2012;3(4):285-99. DOI:10.1002/jrsm.1054
47. Bogdanov AA, Moiseenko FV, Egorenkov VV, et al. Comparison of the efficacy of first-line therapy with different generations of EGFR tyrosine kinase inhibitors in patients with advanced EGFR-associated non-small cell lung cancer: a network meta-analysis of overall survival data. Journal of Modern Oncology. 2021;23(1):116-20 (in Russian). DOI:10.26442/18151434.2021.1.200731
48. Scodes S, Cappuzzo F. Determining the appropriate treatment for different EGFR mutations in non-small cell lung cancer patients. Expert Review of Respiratory Medicine. 2020;14(6):565-76. DOI:10.1080/17476348.2020.1746646
49. Rücker G, Schwarzer G. Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol. 2015;15(1):58. DOI:10.1186/s12874-015-0060-8
50. Mbuagbaw L, Rochwerg B, Jaeschke R, et al. Approaches to interpreting and choosing the best treatments in network meta-analyses. Systematic Reviews. 2017;6(1):79. DOI:10.1186/s13643-017-0473-z
51. Li J, Kwok HF. Current Strategies for Treating NSCLC: From Biological Mechanisms to Clinical Treatment. Cancers (Basel). 2020;12(6):1587. DOI:10.3390/cancers12061587
52. Ventresca M, Schünemann HJ, Macbeth F, et al. Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide. BMC Med Res Methodol. 2020;20(1):113. DOI:10.1186/s12874-020-00964-6
53. Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. Br Med J (Clin Res Ed). 2010;340:c221. DOI:10.1136/bmj.c221
54. Lipworth W. Real-world Data to Generate Evidence About Healthcare Interventions. Asian Bioethics Review. 2019;11(3):289-98. DOI:10.1007/s41649-019-00095-1
55. Bartlett VL, Dhruva SS, Shah ND, et al. Feasibility of Using Real-World Data to Replicate Clinical Trial Evidence. JAMA Network Open. 2019;2(10):e1912869-e. DOI:10.1001/jamanetworkopen.2019.12869
56. Ito K, Morise M, Wakuda K, et al. A multicenter cohort study of osimertinib compared with afatinib as first-line treatment for EGFR-mutated non-small-cell lung cancer from practical dataset: CJLSG1903. ESMO Open. 2021;6(3):100115. DOI:10.1016/j.esmoop.2021.100115
2. Murad MH, Asi N, Alsawas M, Alahdab F. New evidence pyramid. Evidence Based Medicine. 2016;21(4):125-7. DOI:10.1136/ebmed-2016-110401
3. Lockwood C, Oh EG. Systematic reviews: Guidelines, tools and checklists for authors. Nurs Health Sci. 2017;19(3):273-7. DOI:10.1111/nhs.12353
4. Khan KS, Kunz R, Kleijnen J, Antes G. Five steps to conducting a systematic review. J R Soc Med. 2003;96(3):118-21. DOI:10.1258/jrsm.96.3.118
5. Berlin JA, Golub RM. Meta-analysis as Evidence: Building a Better Pyramid. JAMA. 2014;312(6):603-6. DOI:10.1001/jama.2014.8167
6. Bucher HC, Guyatt GH, Griffith LE, Walter SD. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol. 1997;50(6):683-91. DOI:10.1016/s0895-4356(97)00049-8
7. Higgins JP, Whitehead A. Borrowing strength from external trials in a meta-analysis. Stat Med. 1996;15(24):2733-49.
DOI:10.1002/(sici)1097-0258(19961230)15:24<2733::aid-sim562>3.0.co;2-0
8. Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods. 2012;3(2):80-97. DOI:10.1002/jrsm.1037
9. Caldwell DM, Ades AE, Higgins JP. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. Br Med J (Clin Res Ed). 2005;331(7521):897-900. DOI:10.1136/bmj.331.7521.897
10. Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004;23(20):3105-24. DOI:10.1002/sim.1875
11. Faltinsen EG, Storebø OJ, Jakobsen JC, et al. Network meta-analysis: the highest level of medical evidence? BMJ Evid Based Med. 2018;23(2):56-9.
DOI:10.1136/bmjebm-2017-110887
12. Dias S, Caldwell DM. Network meta-analysis explained. Arch Dis Child Fetal Neonatal Ed. 2019;104(1):F8-12. doi:10.1136/archdischild-2018-315224
13. Rouse B, Chaimani A, Li T. Network meta-analysis: an introduction for clinicians. Intern Emerg Med. 2017;12(1):103-11. DOI:10.1007/s11739-016-1583-7
14. Tonin FS, Rotta I, Mendes AM, Pontarolo R. Network meta-analysis: a technique to gather evidence from direct and indirect comparisons. Pharm Pract (Granada). 2017;15(1):943. DOI:10.18549/PharmPract.2017.01.943
15. Ter Veer E, van Oijen MGH, van Laarhoven HWM. The Use of (Network) Meta-Analysis in Clinical Oncology. Front Oncol. 2019;9:822. DOI:10.3389/fonc.2019.00822
16. Zarin W, Veroniki AA, Nincic V, et al. Characteristics and knowledge synthesis approach for 456 network meta-analyses: a scoping review. BMC Med. 2017;15(1):3. DOI:10.1186/s12916-016-0764-6
17. Ge L, Tian J-h, Li X-x, et al. Epidemiology Characteristics, Methodological Assessment and Reporting of Statistical Analysis of Network Meta-Analyses in the Field of Cancer. Sci Rep. 2016;6(1):37208. DOI:10.1038/srep37208
18. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Br Med J (Clin Res Ed). 2009;339:b2535. DOI:10.1136/bmj.b2535
19. Hutton B, Salanti G, Caldwell DM, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162(11):777‑84. DOI:10.7326/m14-2385
20. Higgins JPT, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. Br Med J (Clin Res Ed). 2011;343:d5928. DOI:10.1136/bmj.d5928
21. Rethlefsen ML, Murad MH, Livingston EH. Engaging Medical Librarians to Improve the Quality of Review Articles. JAMA. 2014;312(10):999-1000. DOI:10.1001/jama.2014.9263
22. Li L, Tian J, Tian H, et al. Network meta-analyses could be improved by searching more sources and by involving a librarian. J Clin Epidemiol.2014;67(9):1001-7. DOI:10.1016/j.jclinepi.2014.04.003
23. Altwairgi AK, Booth CM, Hopman WM, Baetz TD. Discordance Between Conclusions Stated in the Abstract and Conclusions in the Article: Analysis of Published Randomized Controlled Trials of Systemic Therapy in Lung Cancer. J Clin Oncol. 2012;30(28):3552-7. DOI:10.1200/jco.2012.41.8319
24. McAuley L, Pham B, Tugwell P, Moher D. Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses? The Lancet. 2000;356(9237):1228-31. DOI:10.1016/S0140-6736(00)02786-0
25. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Br Med J (Clin Res Ed). 1997;315(7109):629-34.
DOI:10.1136/bmj.315.7109.629
26. Trinquart L, Ioannidis JPA, Chatellier G, Ravaud P. A test for reporting bias in trial networks: simulation and case studies. BMC Medical Research Methodology. 2014;14(1):112. DOI:10.1186/1471-2288-14-112
27. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Br Med J (Clin Res Ed). 2003;327(7414):557-60.
DOI:10.1136/bmj.327.7414.557
28. Watt J, Tricco AC, Straus S, et al. Research Techniques Made Simple: Network Meta-Analysis. J Invest Dermatol. 2019;139(1):4-12.e1. DOI:10.1016/j.jid.2018.10.028
29. Higgins J, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions version 6.2. Cochrane, 2021.
30. Cipriani A, Higgins JP, Geddes JR, Salanti G. Conceptual and technical challenges in network meta-analysis. Ann Intern Med. 2013;159(2):130-7.
DOI:10.7326/0003-4819-159-2-201307160-00008
31. IntHout J, Ioannidis JP, Borm GF. Obtaining evidence by a single well-powered trial or several modestly powered trials. Statistical Methods in Medical Research. 2016;25(2):538-52. DOI:10.1177/0962280212461098
32. Serghiou S, Goodman SN. Random-Effects Meta-analysis: Summarizing Evidence With Caveats. JAMA. 2019;321(3):301-2. DOI:10.1001/jama.2018.19684
33. Efthimiou O, Debray TP, van Valkenhoef G, et al. GetReal in network meta-analysis: a review of the methodology. Res Synth Methods. 2016;7(3):236-63. DOI:10.1002/jrsm.1195
34. Hassan S, Ravishankar N, Nair SN. Methodological considerations in network meta-analysis. Int J Med Sci Public Health. 2015;4(5):1-7.
35. Dias S, Ades AE, Welton NJ, et al. Network meta-analysis for decision-making: John Wiley and Sons, 2018.
36. Aronow PM, Miller BT. Foundations of agnostic statistics: Cambridge University Press, 2019.
37. Greco T, Edefonti V, Biondi-Zoccai G, et al. A multilevel approach to network meta-analysis within a frequentist framework. Contemporary Clinical Trials. 2015;42:51‑9. DOI:10.1016/j.cct.2015.03.005
38. Madden LV, Piepho HP, Paul PA. Statistical Models and Methods for Network Meta-Analysis. Phytopathology. 2016;106(8):792-806. DOI:10.1094/phyto-12-15-0342-rvw
39. Kim H, Gurrin L, Ademi Z, Liew D. Overview of methods for comparing the efficacies of drugs in the absence of head-to-head clinical trial data. Brit J Clin Pharm. 2014;77(1):116-21. DOI:10.1111/bcp.12150
40. Ohlssen D, Price KL, Xia HA, et al. Guidance on the implementation and reporting of a drug safety Bayesian network meta-analysis. Pharm Stat. 2014;13(1):55-70. DOI:10.1002/pst.1592
41. Kibret T, Richer D, Beyene J. Bias in identification of the best treatment in a Bayesian network meta-analysis for binary outcome: a simulation study. Clin Epidemiol. 2014;6:451-60. DOI:10.2147/clep.s69660
42. Uhlmann L, Jensen K, Kieser M. Bayesian network meta-analysis for cluster randomized trials with binary outcomes. Res Synth Methods. 2017;8(2):236-50. DOI:10.1002/jrsm.1210
43. Shim SR, Kim SJ, Lee J, Rücker G. Network meta-analysis: application and practice using R software. Epidemiol Health. 2019;41:e2019013. DOI:10.4178/epih.e2019013
44. Seide SE, Jensen K, Kieser M. A comparison of Bayesian and frequentist methods in random-effects network meta-analysis of binary data. Res Synth Methods. 2020;11(3):363-78. DOI:10.1002/jrsm.1397
45. Rücker G, Krahn U, König J, et al. Network meta-analysis using frequentist methods. (netmeta): R package version 1.2-1. 2020
46. van Valkenhoef G, Lu G, de Brock B, et al. Automating network meta-analysis. Res Synth Methods. 2012;3(4):285-99. DOI:10.1002/jrsm.1054
47. Богданов А.А., Моисеенко Ф.В., Егоренков В.В., и др. Сравнение эффективности первой линии терапии разными поколениями ингибиторов EGFR-тирозинкиназы у пациентов с распространенным EGFR-ассоциированным немелкоклеточным раком легкого: сетевой метаанализ данных общей выживаемости. Современная Онкология. 2021;23(1):116-20 [Bogdanov AA, Moiseenko FV, Egorenkov VV, et al. Comparison of the efficacy of first-line therapy with different generations of EGFR tyrosine kinase inhibitors in patients with advanced EGFR-associated non-small cell lung cancer: a network meta-analysis of overall survival data. Journal of Modern Oncology. 2021;23(1):116-20 (in Russian)]. DOI:10.26442/18151434.2021.1.200731
48. Scodes S, Cappuzzo F. Determining the appropriate treatment for different EGFR mutations in non-small cell lung cancer patients. Expert Review of Respiratory Medicine. 2020;14(6):565-76. DOI:10.1080/17476348.2020.1746646
49. Rücker G, Schwarzer G. Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol. 2015;15(1):58. DOI:10.1186/s12874-015-0060-8
50. Mbuagbaw L, Rochwerg B, Jaeschke R, et al. Approaches to interpreting and choosing the best treatments in network meta-analyses. Systematic Reviews. 2017;6(1):79. DOI:10.1186/s13643-017-0473-z
51. Li J, Kwok HF. Current Strategies for Treating NSCLC: From Biological Mechanisms to Clinical Treatment. Cancers (Basel). 2020;12(6):1587. DOI:10.3390/cancers12061587
52. Ventresca M, Schünemann HJ, Macbeth F, et al. Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide. BMC Med Res Methodol. 2020;20(1):113. DOI:10.1186/s12874-020-00964-6
53. Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. Br Med J (Clin Res Ed). 2010;340:c221. DOI:10.1136/bmj.c221
54. Lipworth W. Real-world Data to Generate Evidence About Healthcare Interventions. Asian Bioethics Review. 2019;11(3):289-98. DOI:10.1007/s41649-019-00095-1
55. Bartlett VL, Dhruva SS, Shah ND, et al. Feasibility of Using Real-World Data to Replicate Clinical Trial Evidence. JAMA Network Open. 2019;2(10):e1912869-e. DOI:10.1001/jamanetworkopen.2019.12869
56. Ito K, Morise M, Wakuda K, et al. A multicenter cohort study of osimertinib compared with afatinib as first-line treatment for EGFR-mutated non-small-cell lung cancer from practical dataset: CJLSG1903. ESMO Open. 2021;6(3):100115. DOI:10.1016/j.esmoop.2021.100115
________________________________________________
2. Murad MH, Asi N, Alsawas M, Alahdab F. New evidence pyramid. Evidence Based Medicine. 2016;21(4):125-7. DOI:10.1136/ebmed-2016-110401
3. Lockwood C, Oh EG. Systematic reviews: Guidelines, tools and checklists for authors. Nurs Health Sci. 2017;19(3):273-7. DOI:10.1111/nhs.12353
4. Khan KS, Kunz R, Kleijnen J, Antes G. Five steps to conducting a systematic review. J R Soc Med. 2003;96(3):118-21. DOI:10.1258/jrsm.96.3.118
5. Berlin JA, Golub RM. Meta-analysis as Evidence: Building a Better Pyramid. JAMA. 2014;312(6):603-6. DOI:10.1001/jama.2014.8167
6. Bucher HC, Guyatt GH, Griffith LE, Walter SD. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol. 1997;50(6):683-91. DOI:10.1016/s0895-4356(97)00049-8
7. Higgins JP, Whitehead A. Borrowing strength from external trials in a meta-analysis. Stat Med. 1996;15(24):2733-49.
DOI:10.1002/(sici)1097-0258(19961230)15:24<2733::aid-sim562>3.0.co;2-0
8. Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods. 2012;3(2):80-97. DOI:10.1002/jrsm.1037
9. Caldwell DM, Ades AE, Higgins JP. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. Br Med J (Clin Res Ed). 2005;331(7521):897-900. DOI:10.1136/bmj.331.7521.897
10. Lu G, Ades AE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004;23(20):3105-24. DOI:10.1002/sim.1875
11. Faltinsen EG, Storebø OJ, Jakobsen JC, et al. Network meta-analysis: the highest level of medical evidence? BMJ Evid Based Med. 2018;23(2):56-9.
DOI:10.1136/bmjebm-2017-110887
12. Dias S, Caldwell DM. Network meta-analysis explained. Arch Dis Child Fetal Neonatal Ed. 2019;104(1):F8-12. doi:10.1136/archdischild-2018-315224
13. Rouse B, Chaimani A, Li T. Network meta-analysis: an introduction for clinicians. Intern Emerg Med. 2017;12(1):103-11. DOI:10.1007/s11739-016-1583-7
14. Tonin FS, Rotta I, Mendes AM, Pontarolo R. Network meta-analysis: a technique to gather evidence from direct and indirect comparisons. Pharm Pract (Granada). 2017;15(1):943. DOI:10.18549/PharmPract.2017.01.943
15. Ter Veer E, van Oijen MGH, van Laarhoven HWM. The Use of (Network) Meta-Analysis in Clinical Oncology. Front Oncol. 2019;9:822. DOI:10.3389/fonc.2019.00822
16. Zarin W, Veroniki AA, Nincic V, et al. Characteristics and knowledge synthesis approach for 456 network meta-analyses: a scoping review. BMC Med. 2017;15(1):3. DOI:10.1186/s12916-016-0764-6
17. Ge L, Tian J-h, Li X-x, et al. Epidemiology Characteristics, Methodological Assessment and Reporting of Statistical Analysis of Network Meta-Analyses in the Field of Cancer. Sci Rep. 2016;6(1):37208. DOI:10.1038/srep37208
18. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Br Med J (Clin Res Ed). 2009;339:b2535. DOI:10.1136/bmj.b2535
19. Hutton B, Salanti G, Caldwell DM, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162(11):777‑84. DOI:10.7326/m14-2385
20. Higgins JPT, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. Br Med J (Clin Res Ed). 2011;343:d5928. DOI:10.1136/bmj.d5928
21. Rethlefsen ML, Murad MH, Livingston EH. Engaging Medical Librarians to Improve the Quality of Review Articles. JAMA. 2014;312(10):999-1000. DOI:10.1001/jama.2014.9263
22. Li L, Tian J, Tian H, et al. Network meta-analyses could be improved by searching more sources and by involving a librarian. J Clin Epidemiol.2014;67(9):1001-7. DOI:10.1016/j.jclinepi.2014.04.003
23. Altwairgi AK, Booth CM, Hopman WM, Baetz TD. Discordance Between Conclusions Stated in the Abstract and Conclusions in the Article: Analysis of Published Randomized Controlled Trials of Systemic Therapy in Lung Cancer. J Clin Oncol. 2012;30(28):3552-7. DOI:10.1200/jco.2012.41.8319
24. McAuley L, Pham B, Tugwell P, Moher D. Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses? The Lancet. 2000;356(9237):1228-31. DOI:10.1016/S0140-6736(00)02786-0
25. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Br Med J (Clin Res Ed). 1997;315(7109):629-34.
DOI:10.1136/bmj.315.7109.629
26. Trinquart L, Ioannidis JPA, Chatellier G, Ravaud P. A test for reporting bias in trial networks: simulation and case studies. BMC Medical Research Methodology. 2014;14(1):112. DOI:10.1186/1471-2288-14-112
27. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Br Med J (Clin Res Ed). 2003;327(7414):557-60.
DOI:10.1136/bmj.327.7414.557
28. Watt J, Tricco AC, Straus S, et al. Research Techniques Made Simple: Network Meta-Analysis. J Invest Dermatol. 2019;139(1):4-12.e1. DOI:10.1016/j.jid.2018.10.028
29. Higgins J, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions version 6.2. Cochrane, 2021.
30. Cipriani A, Higgins JP, Geddes JR, Salanti G. Conceptual and technical challenges in network meta-analysis. Ann Intern Med. 2013;159(2):130-7.
DOI:10.7326/0003-4819-159-2-201307160-00008
31. IntHout J, Ioannidis JP, Borm GF. Obtaining evidence by a single well-powered trial or several modestly powered trials. Statistical Methods in Medical Research. 2016;25(2):538-52. DOI:10.1177/0962280212461098
32. Serghiou S, Goodman SN. Random-Effects Meta-analysis: Summarizing Evidence With Caveats. JAMA. 2019;321(3):301-2. DOI:10.1001/jama.2018.19684
33. Efthimiou O, Debray TP, van Valkenhoef G, et al. GetReal in network meta-analysis: a review of the methodology. Res Synth Methods. 2016;7(3):236-63. DOI:10.1002/jrsm.1195
34. Hassan S, Ravishankar N, Nair SN. Methodological considerations in network meta-analysis. Int J Med Sci Public Health. 2015;4(5):1-7.
35. Dias S, Ades AE, Welton NJ, et al. Network meta-analysis for decision-making: John Wiley and Sons, 2018.
36. Aronow PM, Miller BT. Foundations of agnostic statistics: Cambridge University Press, 2019.
37. Greco T, Edefonti V, Biondi-Zoccai G, et al. A multilevel approach to network meta-analysis within a frequentist framework. Contemporary Clinical Trials. 2015;42:51‑9. DOI:10.1016/j.cct.2015.03.005
38. Madden LV, Piepho HP, Paul PA. Statistical Models and Methods for Network Meta-Analysis. Phytopathology. 2016;106(8):792-806. DOI:10.1094/phyto-12-15-0342-rvw
39. Kim H, Gurrin L, Ademi Z, Liew D. Overview of methods for comparing the efficacies of drugs in the absence of head-to-head clinical trial data. Brit J Clin Pharm. 2014;77(1):116-21. DOI:10.1111/bcp.12150
40. Ohlssen D, Price KL, Xia HA, et al. Guidance on the implementation and reporting of a drug safety Bayesian network meta-analysis. Pharm Stat. 2014;13(1):55-70. DOI:10.1002/pst.1592
41. Kibret T, Richer D, Beyene J. Bias in identification of the best treatment in a Bayesian network meta-analysis for binary outcome: a simulation study. Clin Epidemiol. 2014;6:451-60. DOI:10.2147/clep.s69660
42. Uhlmann L, Jensen K, Kieser M. Bayesian network meta-analysis for cluster randomized trials with binary outcomes. Res Synth Methods. 2017;8(2):236-50. DOI:10.1002/jrsm.1210
43. Shim SR, Kim SJ, Lee J, Rücker G. Network meta-analysis: application and practice using R software. Epidemiol Health. 2019;41:e2019013. DOI:10.4178/epih.e2019013
44. Seide SE, Jensen K, Kieser M. A comparison of Bayesian and frequentist methods in random-effects network meta-analysis of binary data. Res Synth Methods. 2020;11(3):363-78. DOI:10.1002/jrsm.1397
45. Rücker G, Krahn U, König J, et al. Network meta-analysis using frequentist methods. (netmeta): R package version 1.2-1. 2020
46. van Valkenhoef G, Lu G, de Brock B, et al. Automating network meta-analysis. Res Synth Methods. 2012;3(4):285-99. DOI:10.1002/jrsm.1054
47. Bogdanov AA, Moiseenko FV, Egorenkov VV, et al. Comparison of the efficacy of first-line therapy with different generations of EGFR tyrosine kinase inhibitors in patients with advanced EGFR-associated non-small cell lung cancer: a network meta-analysis of overall survival data. Journal of Modern Oncology. 2021;23(1):116-20 (in Russian). DOI:10.26442/18151434.2021.1.200731
48. Scodes S, Cappuzzo F. Determining the appropriate treatment for different EGFR mutations in non-small cell lung cancer patients. Expert Review of Respiratory Medicine. 2020;14(6):565-76. DOI:10.1080/17476348.2020.1746646
49. Rücker G, Schwarzer G. Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol. 2015;15(1):58. DOI:10.1186/s12874-015-0060-8
50. Mbuagbaw L, Rochwerg B, Jaeschke R, et al. Approaches to interpreting and choosing the best treatments in network meta-analyses. Systematic Reviews. 2017;6(1):79. DOI:10.1186/s13643-017-0473-z
51. Li J, Kwok HF. Current Strategies for Treating NSCLC: From Biological Mechanisms to Clinical Treatment. Cancers (Basel). 2020;12(6):1587. DOI:10.3390/cancers12061587
52. Ventresca M, Schünemann HJ, Macbeth F, et al. Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide. BMC Med Res Methodol. 2020;20(1):113. DOI:10.1186/s12874-020-00964-6
53. Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. Br Med J (Clin Res Ed). 2010;340:c221. DOI:10.1136/bmj.c221
54. Lipworth W. Real-world Data to Generate Evidence About Healthcare Interventions. Asian Bioethics Review. 2019;11(3):289-98. DOI:10.1007/s41649-019-00095-1
55. Bartlett VL, Dhruva SS, Shah ND, et al. Feasibility of Using Real-World Data to Replicate Clinical Trial Evidence. JAMA Network Open. 2019;2(10):e1912869-e. DOI:10.1001/jamanetworkopen.2019.12869
56. Ito K, Morise M, Wakuda K, et al. A multicenter cohort study of osimertinib compared with afatinib as first-line treatment for EGFR-mutated non-small-cell lung cancer from practical dataset: CJLSG1903. ESMO Open. 2021;6(3):100115. DOI:10.1016/j.esmoop.2021.100115
Авторы
А.А. Богданов*, Ан.А. Богданов
ГБУЗ «Санкт-Петербургский клинический научно-практический центр специализированных видов медицинской помощи (онкологический)», Санкт-Петербург, Россия
*a.bogdanov@oncocentre.ru
Saint Petersburg Clinical Research and Practice Centre for Specialized Types of Medical Care (Oncological), Saint Petersburg, Russia
ГБУЗ «Санкт-Петербургский клинический научно-практический центр специализированных видов медицинской помощи (онкологический)», Санкт-Петербург, Россия
*a.bogdanov@oncocentre.ru
________________________________________________
Saint Petersburg Clinical Research and Practice Centre for Specialized Types of Medical Care (Oncological), Saint Petersburg, Russia
Цель портала OmniDoctor – предоставление профессиональной информации врачам, провизорам и фармацевтам.