Research progress on evidence synthesis of randomized and non-randomized studies of interventions_Chinese Journal of Evidence-Based Medicine

Authors：

MEI Fan ^1,2,3,4,5 ,  YAO Minghong ^1,2,3,4,5 , WANG Yuning ^1,2,3,4,5 , LI Ling ^1,2,3,4,5 , SUN Xin ^1,2,3,4,5

1. Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;
2. Chinese Evidence-Based Medicine Center, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;
3. NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, P. R. China;
4. China Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, P. R. China;
5. Cochrane China Center and MAGIC China Center, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

YAO Minghong, Email: ymhldjxa@sina.com

Keywords：

Randomized controlled trial; Non-randomized intervention study; Evidence synthesis; Evidence quality

DOI：

10.7507/1672-2531.202304041

Video：

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Abstract Full text Figures/Tables Video References Cited by

Evidence synthesis serves as a bridge between clinical practice and the best available evidence. Evidence synthesis based on high-quality randomized controlled trials is generally considered the highest level of evidence, but its external validity is limited. In some scenarios, the inclusion of non-randomized intervention studies (NRSI) in evidence synthesis may further supplement or even replace randomized controlled trial evidence, such as assessing intervention effectiveness and rare events in a broader population to provide more information for health care decision-making. With the rapid development of real-world data and the improvement of statistical analysis methods, real-world evidence, as an important source of evidence for NRSI, has accelerated the development of high-quality NRSI. However, there are numerous challenges in integrating evidence from randomized and non-randomized intervention studies due to selection and confounding biases caused by the lack of randomization. Based on previous studies, this paper systematically examines the current status of integrated randomized and non-randomized intervention studies, including integration premise, timing, methods, and result interpretation, in order to provide references for researchers and policy-makers to correctly use non-randomized research evidence and further promote optimal evidence generation and clinical practice translation.

Citation： MEI Fan, YAO Minghong, WANG Yuning, LI Ling, SUN Xin. Research progress on evidence synthesis of randomized and non-randomized studies of interventions. Chinese Journal of Evidence-Based Medicine, 2023, 23(9): 1102-1109. doi: 10.7507/1672-2531.202304041 Copy

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2.	Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med, 2000, 342(25): 1887-1892.
3.	Black N. Why we need observational studies to evaluate the effectiveness of health care. BMJ, 1996, 312(7040): 1215-1218.
4.	Saldanha IJ, Adam GP, Bañez LL, et al. Inclusion of nonrandomized studies of interventions in systematic reviews of interventions: updated guidance from the Agency for Health Care Research and Quality Effective Health Care program. J Clin Epidemiol, 2022, 152: 300-306.
5.	Bothwell LE, Greene JA, Podolsky SH, et al. Assessing the gold standard-lessons from the history of RCTs. N Engl J Med, 2016, 374(22): 2175-2181.
6.	Rothwell PM. External validity of randomised controlled trials: "to whom do the results of this trial apply?". Lancet, 2005, 365(9453): 82-93.
7.	Yang W, Zilov A, Soewondo P, et al. Observational studies: going beyond the boundaries of randomized controlled trials. Diabetes Res Clin Pract, 2010, 88 (Suppl 1): S3-S9.
8.	Higgins JP, Ramsay C, Reeves BC, et al. Issues relating to study design and risk of bias when including non-randomized studies in systematic reviews on the effects of interventions. Res Synth Methods, 2013, 4(1): 12-25.
9.	Cumpston M, Li T, Page MJ, et al. Updated guidance for trusted systematic reviews: a new edition of the Cochrane handbook for systematic reviews of interventions. Cochrane Database Syst Rev, 2019, (10): ED000142.
10.	Schurman B. The framework for FDA’s real-world evidence program. Applied Clinical Trials, 2019.
11.	Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence - what is it and what can it tell us? N Engl J Med, 2016, 375(23): 2293-2297.
12.	Golder S, Loke YK, Bland M. Meta-analyses of adverse effects data derived from randomised controlled trials as compared to observational studies: methodological overview. PLoS Med, 2011, 8(5): e1001026.
13.	Hannan EL. Randomized clinical trials and observational studies: guidelines for assessing respective strengths and limitations. JACC Cardiovasc Interv, 2008, 1(3): 211-217.
14.	孙鑫, 谭婧, 唐立, 等. 重新认识真实世界研究. 中国循证医学杂志, 2017, 17(2): 126-130.
15.	Benson K, Hartz AJ. A comparison of observational studies and randomized, controlled trials. N Engl J Med, 2000, 342(25): 1878-1886.
16.	The value of evidence synthesis. Nat Hum Behav, 2021, 5(5): 539.
17.	Yoshida K, Solomon DH, Kim SC. Active-comparator design and new-user design in observational studies. Nat Rev Rheumatol, 2015, 11(7): 437-441.
18.	Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res, 2011, 46(3): 399-424.
19.	Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology, 2000, 11(5): 550-560.
20.	Ewald H, Ioannidis JPA, Ladanie A, et al. Nonrandomized studies using causal-modeling may give different answers than RCTs: a meta-epidemiological study. J Clin Epidemiol, 2020, 118: 29-41.
21.	Kuss O, Legler T, Börgermann J. Treatments effects from randomized trials and propensity score analyses were similar in similar populations in an example from cardiac surgery. J Clin Epidemiol, 2011, 64(10): 1076-1084.
22.	Lonjon G, Boutron I, Trinquart L, et al. Comparison of treatment effect estimates from prospective nonrandomized studies with propensity score analysis and randomized controlled trials of surgical procedures. Ann Surg, 2014, 259(1): 18-25.
23.	Rosenberg G, Baerwald EJ, Holden G. Cross design synthesis-a new strategy for medical effectiveness research (1992)-United-States-General-Accounting-Office. Soc Work Health Care, 1995, 20(3): 113-116.
24.	Mayo-Wilson E, Li T, Fusco N, et al. Practical guidance for using multiple data sources in systematic reviews and meta-analyses (with examples from the MUDS study). Res Synth Methods, 2018, 9(1): 2-12.
25.	Cuello-Garcia CA, Santesso N, Morgan RL, et al. GRADE guidance 24 optimizing the integration of randomized and non-randomized studies of interventions in evidence syntheses and health guidelines. J Clin Epidemiol, 2022, 142: 200-208.
26.	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.
27.	Schünemann HJ, Tugwell P, Reeves BC, et al. Non-randomized studies as a source of complementary, sequential or replacement evidence for randomized controlled trials in systematic reviews on the effects of interventions. Res Synth Methods, 2013, 4(1): 49-62.
28.	Page MJ, McKenzie JE, Chau M, et al. Methods to select results to include in meta-analyses deserve more consideration in systematic reviews. J Clin Epidemiol, 2015, 68(11): 1282-1291.
29.	Schwingshackl L, Balduzzi S, Beyerbach J, et al. Evaluating agreement between bodies of evidence from randomised controlled trials and cohort studies in nutrition research: meta-epidemiological study. BMJ, 2021, 374: n1864.
30.	Moneer O, Daly G, Skydel JJ, et al. Agreement of treatment effects from observational studies and randomized controlled trials evaluating hydroxychloroquine, lopinavir-ritonavir, or dexamethasone for COVID-19: meta-epidemiological study. BMJ, 2022, 377: e069400.
31.	Bröckelmann N, Balduzzi S, Harms L, et al. Evaluating agreement between bodies of evidence from randomized controlled trials and cohort studies in medical research: a meta-epidemiological study. BMC Med, 2022, 20(1): 174.
32.	Hong YD, Jansen JP, Guerino J, et al. Comparative effectiveness and safety of pharmaceuticals assessed in observational studies compared with randomized controlled trials. BMC Med, 2021, 19(1): 307.
33.	Norris SL, Atkins D, Bruening W, et al. Observational studies in systematic reviews of comparative effectiveness: AHRQ and the effective health care program. J Clin Epidemiol, 2011, 64(11): 1178-1186.
34.	Abraham NS, Byrne CJ, Young JM, et al. Meta-analysis of well-designed nonrandomized comparative studies of surgical procedures is as good as randomized controlled trials. J Clin Epidemiol, 2010, 63(3): 238-245.
35.	Faber T, Ravaud P, Riveros C, et al. Meta-analyses including non-randomized studies of therapeutic interventions: a methodological review. BMC Med Res Methodol, 2016, 16: 35.
36.	Reeves BC, Deeks JJ, Higgins JPT, et al. Chapter 24: Including non-randomized studies on intervention effects. In: Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). Cochrane, 2022.
37.	Reeves BC, Higgins JP, Ramsay C, et al. An introduction to methodological issues when including non-randomised studies in systematic reviews on the effects of interventions. Res Synth Methods, 2013, 4(1): 1-11.
38.	Gargiulo G, Sannino A, Capodanno D, et al. Transcatheter aortic valve implantation versus surgical aortic valve replacement: a systematic review and meta-analysis. Ann Intern Med, 2016, 165(5): 334-344.
39.	Hopley C, Stengel D, Ekkernkamp A, et al. Primary total hip arthroplasty versus hemiarthroplasty for displaced intracapsular hip fractures in older patients: systematic review. BMJ, 2010, 340: c2332.
40.	Azad MB, Abou-Setta AM, Chauhan BF, et al. Nonnutritive sweeteners and cardiometabolic health: a systematic review and meta-analysis of randomized controlled trials and prospective cohort studies. CMAJ, 2017, 189(28): E929-E939.
41.	Aburto NJ, Ziolkovska A, Hooper L, et al. Effect of lower sodium intake on health: systematic review and meta-analyses. BMJ, 2013, 346: f1326.
42.	Alipanah N, Jarlsberg L, Miller C, et al. Adherence interventions and outcomes of tuberculosis treatment: a systematic review and meta-analysis of trials and observational studies. PLoS Med, 2018, 15(7): e1002595.
43.	Bun RS, Scheer J, Guillo S, et al. Meta-analyses frequently pooled different study types together: a meta-epidemiological study. J Clin Epidemiol, 2020, 118: 18-28.
44.	Verde PE, Ohmann C. Combining randomized and non-randomized evidence in clinical research: a review of methods and applications. Res Synth Methods, 2015, 6(1): 45-62.
45.	Djulbegovic B, Guyatt GH. Progress in evidence-based medicine: a quarter century on. Lancet, 2017, 390(10092): 415-423.
46.	Ijaz S, Verbeek JH, Mischke C, et al. Inclusion of nonrandomized studies in Cochrane systematic reviews was found to be in need of improvement. J Clin Epidemiol, 2014, 67(6): 645-653.
47.	Egger M, Smith GD, Phillips AN. Meta-analysis: principles and procedures. BMJ, 1997, 315(7121): 1533-1537.
48.	Campbell M, McKenzie JE, Sowden A, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ, 2020, 368: l6890.
49.	Higgins JPT, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions version 6.3 (updated February 2022). Cochrane, 2022.
50.	Cuello-Garcia CA, Morgan RL, Brozek J, et al. A scoping review and survey provides the rationale, perceptions, and preferences for the integration of randomized and nonrandomized studies in evidence syntheses and GRADE assessments. J Clin Epidemiol, 2018, 98: 33-40.
51.	Gurevitch J, Koricheva J, Nakagawa S, et al. Meta-analysis and the science of research synthesis. Nature, 2018, 555(7695): 175-182.
52.	Verde PE. A bias-corrected meta-analysis model for combining, studies of different types and quality. Biom J, 2021, 63(2): 406-422.
53.	Verde PE. The hierarchical metaregression approach and learning from clinical evidence. Biom J, 2019, 61(3): 535-557.
54.	Schmitz S, Adams R, Walsh C. Incorporating data from various trial designs into a mixed treatment comparison model. Stat Med, 2013, 32(17): 2935-2949.
55.	Ferguson J, Alvarez-Iglesias A, Newell J, et al. Joint incorporation of randomised and observational evidence in estimating treatment effects. Stat Methods Med Res, 2019, 28(1): 235-247.
56.	Jenkins DA, Hussein H, Martina R, et al. Methods for the inclusion of real-world evidence in network meta-analysis. BMC Med Res Methodol, 2021, 21(1): 207.
57.	Efthimiou O, Mavridis D, Debray TP, et al. Combining randomized and non-randomized evidence in network meta-analysis. Stat Med, 2017, 36(8): 1210-1226.
58.	Turner RM, Spiegelhalter DJ, Smith GC, et al. Bias modelling in evidence synthesis. J R Stat Soc Ser A Stat Soc, 2009, 172(1): 21-47.
59.	Schnell-Inderst P, Iglesias CP, Arvandi M, et al. A bias-adjusted evidence synthesis of RCT and observational data: the case of total hip replacement. Health Econ, 2017, 26(Suppl 1): 46-69.
60.	Zhang K, Arora P, Sati N, et al. Characteristics and methods of incorporating randomized and nonrandomized evidence in network meta-analyses: a scoping review. J Clin Epidemiol, 2019, 113: 1-10.
61.	Yao M, Wang Y, Mei F, et al. Methods for the inclusion of real-world evidence in a rare events meta-analysis of randomized controlled trials. J Clin Med, 2023, 12(4): 1690.
62.	Verbeek JH, Whaley P, Morgan RL, et al. An approach to quantifying the potential importance of residual confounding in systematic reviews of observational studies: a GRADE concept paper. Environ Int, 2021, 157: 106868.
63.	Schwingshackl L, Brockelmann N, Beyerbach J, et al. An empirical evaluation of the impact scenario of pooling bodies of evidence from randomized controlled trials and cohort studies in nutrition research. Adv Nutr, 2022, 13(5): 1774-1786.
64.	Brockelmann N, Stadelmaier J, Harms L, et al. An empirical evaluation of the impact scenario of pooling bodies of evidence from randomized controlled trials and cohort studies in medical research. BMC Med, 2022, 20(1): 355.
65.	Guyatt G, Oxman AD, Akl EA, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol, 2011, 64(4): 383-394.
66.	Sterne JAC, Savovic J, Page MJ, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ, 2019, 366: l4898.
67.	Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 2016, 355: i4919.
68.	Schünemann HJ, Cuello C, Akl EA, et al. GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence. J Clin Epidemiol, 2019, 111: 105-114.
69.	Schwingshackl L, Nagavci B, Stadelmaier J, et al. Pooling of cohort studies and RCTs affects GRADE certainty of evidence in nutrition research. J Clin Epidemiol, 2022, 147: 151-159.

1. Armstrong K. Methods in comparative effectiveness research. J Clin Oncol, 2012, 30(34): 4208-4214.
2. Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med, 2000, 342(25): 1887-1892.
3. Black N. Why we need observational studies to evaluate the effectiveness of health care. BMJ, 1996, 312(7040): 1215-1218.
4. Saldanha IJ, Adam GP, Bañez LL, et al. Inclusion of nonrandomized studies of interventions in systematic reviews of interventions: updated guidance from the Agency for Health Care Research and Quality Effective Health Care program. J Clin Epidemiol, 2022, 152: 300-306.
5. Bothwell LE, Greene JA, Podolsky SH, et al. Assessing the gold standard-lessons from the history of RCTs. N Engl J Med, 2016, 374(22): 2175-2181.
6. Rothwell PM. External validity of randomised controlled trials: "to whom do the results of this trial apply?". Lancet, 2005, 365(9453): 82-93.
7. Yang W, Zilov A, Soewondo P, et al. Observational studies: going beyond the boundaries of randomized controlled trials. Diabetes Res Clin Pract, 2010, 88 (Suppl 1): S3-S9.
8. Higgins JP, Ramsay C, Reeves BC, et al. Issues relating to study design and risk of bias when including non-randomized studies in systematic reviews on the effects of interventions. Res Synth Methods, 2013, 4(1): 12-25.
9. Cumpston M, Li T, Page MJ, et al. Updated guidance for trusted systematic reviews: a new edition of the Cochrane handbook for systematic reviews of interventions. Cochrane Database Syst Rev, 2019, (10): ED000142.
10. Schurman B. The framework for FDA’s real-world evidence program. Applied Clinical Trials, 2019.
11. Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence - what is it and what can it tell us? N Engl J Med, 2016, 375(23): 2293-2297.
12. Golder S, Loke YK, Bland M. Meta-analyses of adverse effects data derived from randomised controlled trials as compared to observational studies: methodological overview. PLoS Med, 2011, 8(5): e1001026.
13. Hannan EL. Randomized clinical trials and observational studies: guidelines for assessing respective strengths and limitations. JACC Cardiovasc Interv, 2008, 1(3): 211-217.
14. 孙鑫, 谭婧, 唐立, 等. 重新认识真实世界研究. 中国循证医学杂志, 2017, 17(2): 126-130.
15. Benson K, Hartz AJ. A comparison of observational studies and randomized, controlled trials. N Engl J Med, 2000, 342(25): 1878-1886.
16. The value of evidence synthesis. Nat Hum Behav, 2021, 5(5): 539.
17. Yoshida K, Solomon DH, Kim SC. Active-comparator design and new-user design in observational studies. Nat Rev Rheumatol, 2015, 11(7): 437-441.
18. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res, 2011, 46(3): 399-424.
19. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology, 2000, 11(5): 550-560.
20. Ewald H, Ioannidis JPA, Ladanie A, et al. Nonrandomized studies using causal-modeling may give different answers than RCTs: a meta-epidemiological study. J Clin Epidemiol, 2020, 118: 29-41.
21. Kuss O, Legler T, Börgermann J. Treatments effects from randomized trials and propensity score analyses were similar in similar populations in an example from cardiac surgery. J Clin Epidemiol, 2011, 64(10): 1076-1084.
22. Lonjon G, Boutron I, Trinquart L, et al. Comparison of treatment effect estimates from prospective nonrandomized studies with propensity score analysis and randomized controlled trials of surgical procedures. Ann Surg, 2014, 259(1): 18-25.
23. Rosenberg G, Baerwald EJ, Holden G. Cross design synthesis-a new strategy for medical effectiveness research (1992)-United-States-General-Accounting-Office. Soc Work Health Care, 1995, 20(3): 113-116.
24. Mayo-Wilson E, Li T, Fusco N, et al. Practical guidance for using multiple data sources in systematic reviews and meta-analyses (with examples from the MUDS study). Res Synth Methods, 2018, 9(1): 2-12.
25. Cuello-Garcia CA, Santesso N, Morgan RL, et al. GRADE guidance 24 optimizing the integration of randomized and non-randomized studies of interventions in evidence syntheses and health guidelines. J Clin Epidemiol, 2022, 142: 200-208.
26. 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.
27. Schünemann HJ, Tugwell P, Reeves BC, et al. Non-randomized studies as a source of complementary, sequential or replacement evidence for randomized controlled trials in systematic reviews on the effects of interventions. Res Synth Methods, 2013, 4(1): 49-62.
28. Page MJ, McKenzie JE, Chau M, et al. Methods to select results to include in meta-analyses deserve more consideration in systematic reviews. J Clin Epidemiol, 2015, 68(11): 1282-1291.
29. Schwingshackl L, Balduzzi S, Beyerbach J, et al. Evaluating agreement between bodies of evidence from randomised controlled trials and cohort studies in nutrition research: meta-epidemiological study. BMJ, 2021, 374: n1864.
30. Moneer O, Daly G, Skydel JJ, et al. Agreement of treatment effects from observational studies and randomized controlled trials evaluating hydroxychloroquine, lopinavir-ritonavir, or dexamethasone for COVID-19: meta-epidemiological study. BMJ, 2022, 377: e069400.
31. Bröckelmann N, Balduzzi S, Harms L, et al. Evaluating agreement between bodies of evidence from randomized controlled trials and cohort studies in medical research: a meta-epidemiological study. BMC Med, 2022, 20(1): 174.
32. Hong YD, Jansen JP, Guerino J, et al. Comparative effectiveness and safety of pharmaceuticals assessed in observational studies compared with randomized controlled trials. BMC Med, 2021, 19(1): 307.
33. Norris SL, Atkins D, Bruening W, et al. Observational studies in systematic reviews of comparative effectiveness: AHRQ and the effective health care program. J Clin Epidemiol, 2011, 64(11): 1178-1186.
34. Abraham NS, Byrne CJ, Young JM, et al. Meta-analysis of well-designed nonrandomized comparative studies of surgical procedures is as good as randomized controlled trials. J Clin Epidemiol, 2010, 63(3): 238-245.
35. Faber T, Ravaud P, Riveros C, et al. Meta-analyses including non-randomized studies of therapeutic interventions: a methodological review. BMC Med Res Methodol, 2016, 16: 35.
36. Reeves BC, Deeks JJ, Higgins JPT, et al. Chapter 24: Including non-randomized studies on intervention effects. In: Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). Cochrane, 2022.
37. Reeves BC, Higgins JP, Ramsay C, et al. An introduction to methodological issues when including non-randomised studies in systematic reviews on the effects of interventions. Res Synth Methods, 2013, 4(1): 1-11.
38. Gargiulo G, Sannino A, Capodanno D, et al. Transcatheter aortic valve implantation versus surgical aortic valve replacement: a systematic review and meta-analysis. Ann Intern Med, 2016, 165(5): 334-344.
39. Hopley C, Stengel D, Ekkernkamp A, et al. Primary total hip arthroplasty versus hemiarthroplasty for displaced intracapsular hip fractures in older patients: systematic review. BMJ, 2010, 340: c2332.
40. Azad MB, Abou-Setta AM, Chauhan BF, et al. Nonnutritive sweeteners and cardiometabolic health: a systematic review and meta-analysis of randomized controlled trials and prospective cohort studies. CMAJ, 2017, 189(28): E929-E939.
41. Aburto NJ, Ziolkovska A, Hooper L, et al. Effect of lower sodium intake on health: systematic review and meta-analyses. BMJ, 2013, 346: f1326.
42. Alipanah N, Jarlsberg L, Miller C, et al. Adherence interventions and outcomes of tuberculosis treatment: a systematic review and meta-analysis of trials and observational studies. PLoS Med, 2018, 15(7): e1002595.
43. Bun RS, Scheer J, Guillo S, et al. Meta-analyses frequently pooled different study types together: a meta-epidemiological study. J Clin Epidemiol, 2020, 118: 18-28.
44. Verde PE, Ohmann C. Combining randomized and non-randomized evidence in clinical research: a review of methods and applications. Res Synth Methods, 2015, 6(1): 45-62.
45. Djulbegovic B, Guyatt GH. Progress in evidence-based medicine: a quarter century on. Lancet, 2017, 390(10092): 415-423.
46. Ijaz S, Verbeek JH, Mischke C, et al. Inclusion of nonrandomized studies in Cochrane systematic reviews was found to be in need of improvement. J Clin Epidemiol, 2014, 67(6): 645-653.
47. Egger M, Smith GD, Phillips AN. Meta-analysis: principles and procedures. BMJ, 1997, 315(7121): 1533-1537.
48. Campbell M, McKenzie JE, Sowden A, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ, 2020, 368: l6890.
49. Higgins JPT, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions version 6.3 (updated February 2022). Cochrane, 2022.
50. Cuello-Garcia CA, Morgan RL, Brozek J, et al. A scoping review and survey provides the rationale, perceptions, and preferences for the integration of randomized and nonrandomized studies in evidence syntheses and GRADE assessments. J Clin Epidemiol, 2018, 98: 33-40.
51. Gurevitch J, Koricheva J, Nakagawa S, et al. Meta-analysis and the science of research synthesis. Nature, 2018, 555(7695): 175-182.
52. Verde PE. A bias-corrected meta-analysis model for combining, studies of different types and quality. Biom J, 2021, 63(2): 406-422.
53. Verde PE. The hierarchical metaregression approach and learning from clinical evidence. Biom J, 2019, 61(3): 535-557.
54. Schmitz S, Adams R, Walsh C. Incorporating data from various trial designs into a mixed treatment comparison model. Stat Med, 2013, 32(17): 2935-2949.
55. Ferguson J, Alvarez-Iglesias A, Newell J, et al. Joint incorporation of randomised and observational evidence in estimating treatment effects. Stat Methods Med Res, 2019, 28(1): 235-247.
56. Jenkins DA, Hussein H, Martina R, et al. Methods for the inclusion of real-world evidence in network meta-analysis. BMC Med Res Methodol, 2021, 21(1): 207.
57. Efthimiou O, Mavridis D, Debray TP, et al. Combining randomized and non-randomized evidence in network meta-analysis. Stat Med, 2017, 36(8): 1210-1226.
58. Turner RM, Spiegelhalter DJ, Smith GC, et al. Bias modelling in evidence synthesis. J R Stat Soc Ser A Stat Soc, 2009, 172(1): 21-47.
59. Schnell-Inderst P, Iglesias CP, Arvandi M, et al. A bias-adjusted evidence synthesis of RCT and observational data: the case of total hip replacement. Health Econ, 2017, 26(Suppl 1): 46-69.
60. Zhang K, Arora P, Sati N, et al. Characteristics and methods of incorporating randomized and nonrandomized evidence in network meta-analyses: a scoping review. J Clin Epidemiol, 2019, 113: 1-10.
61. Yao M, Wang Y, Mei F, et al. Methods for the inclusion of real-world evidence in a rare events meta-analysis of randomized controlled trials. J Clin Med, 2023, 12(4): 1690.
62. Verbeek JH, Whaley P, Morgan RL, et al. An approach to quantifying the potential importance of residual confounding in systematic reviews of observational studies: a GRADE concept paper. Environ Int, 2021, 157: 106868.
63. Schwingshackl L, Brockelmann N, Beyerbach J, et al. An empirical evaluation of the impact scenario of pooling bodies of evidence from randomized controlled trials and cohort studies in nutrition research. Adv Nutr, 2022, 13(5): 1774-1786.
64. Brockelmann N, Stadelmaier J, Harms L, et al. An empirical evaluation of the impact scenario of pooling bodies of evidence from randomized controlled trials and cohort studies in medical research. BMC Med, 2022, 20(1): 355.
65. Guyatt G, Oxman AD, Akl EA, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol, 2011, 64(4): 383-394.
66. Sterne JAC, Savovic J, Page MJ, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ, 2019, 366: l4898.
67. Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 2016, 355: i4919.
68. Schünemann HJ, Cuello C, Akl EA, et al. GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence. J Clin Epidemiol, 2019, 111: 105-114.
69. Schwingshackl L, Nagavci B, Stadelmaier J, et al. Pooling of cohort studies and RCTs affects GRADE certainty of evidence in nutrition research. J Clin Epidemiol, 2022, 147: 151-159.

Chinese Journal of Evidence-Based Medicine

Research progress on evidence synthesis of randomized and non-randomized studies of interventions

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