Progress of next-generation population-adjusted indirect comparison methods_Chinese Journal of Evidence-Based Medicine

Authors：

ZHAN Haoran ^1,2,3 , WEI Xia ^1,2,3 , GONG Chao ^1,2,3 , ZHANG Ye ⁴ ,  YANG Li ^1,2,3

1. School of Public Health, Peking University, Beijing 100191, P. R. China;
2. Beijing Institute for Health Development, Peking University, Beijing 100191, P. R. China;
3. Key Laboratory of Health System Reform and Governance, National Health Commission of China (Peking University), Beijing 100191, P. R. China;
4. School of Population and Health, Renmin University of China, Beijing 100872, P. R. China;

Corresponding?author：

YANG Li, Email: lyang@bjmu.edu.cn

Keywords：

Indirect comparison; Population-adjusted indirect comparison; Two-stage matching-adjusted indirect comparison; Multilevel network meta-regression; Network meta-interpolation; G-computation; Multiple imputation marginalization

DOI：

10.7507/1672-2531.202601049

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To review the methodological evolution, applicable scenarios, and HTA use of next-generation population-adjusted indirect comparison methods. Methods Building on traditional indirect comparisons and first-generation population-adjusted methods, this review summarizes four major development paths—weight optimization, unified modelling, marginalization, and subgroup interpolation—and discusses the principles, assumptions, strengths, and limitations of two-stage matching-adjusted indirect comparison (2SMAIC), multilevel network meta-regression (ML-NMR), G-computation, multiple imputation marginalization (MIM), and network meta-interpolation (NMI). Publicly available HTA cases were also examined to identify major appraisal concerns. Results Compared with traditional matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC), these next-generation methods better address complex evidence networks, transportability to target populations, and incompatibility between conditional and marginal effects. ML-NMR is most useful for anchored evidence networks with multiple studies; 2SMAIC mainly improves precision within the MAIC framework; G-computation and MIM obtain decision-relevant marginal effects through model-based standardization; and NMI enables limited adjustment using subgroup results when individual patient data are unavailable. Published applications remain concentrated in pharmaceuticals. HTA agencies are cautiously open to these methods, but focus on target-population relevance, covariate overlap, key assumptions, and uncertainty analyses. Conclusions Next-generation population-adjusted indirect comparison methods substantially expand the toolkit for evidence generation when head-to-head trials are unavailable, but they do not remove the dependence on data quality and methodological assumptions. Method choice should therefore be aligned with the evidence structure, data availability, and decision question, with transparent reporting and sensitivity analyses.

Citation： ZHAN Haoran, WEI Xia, GONG Chao, ZHANG Ye, YANG Li. Progress of next-generation population-adjusted indirect comparison methods. Chinese Journal of Evidence-Based Medicine, 2026, 26(8): 966-974. doi: 10.7507/1672-2531.202601049 Copy

1.	Bucher HC, Guyatt GH, Griffith LE, et al. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials[J]. J Clin Epidemiol, 1997, 50(6): 683-691.
2.	Wells GA, Sultan SA, Chen L, et al. Indirect evidence: indirect treatment comparisons in meta-analysis[Z]. Ottawa: Canadian Agency for Drugs and Technologies in Health, 2009.
3.	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[J]. Res Synth Methods, 2012, 3(2): 80-97.
4.	Jansen JP, Naci H. Is network meta-analysis as valid as standard pairwise meta-analysis. It all depends on the distribution of effect modifiers[J]. BMC Med, 2013, 11: 159.
5.	Cipriani A, Higgins JP, Geddes JR, et al. Conceptual and technical challenges in network meta-analysis[J]. Ann Intern Med, 2013, 159(2): 130-137.
6.	Song F, Harvey I, Lilford R. Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions[J]. J Clin Epidemiol, 2008, 61(5): 455-463.
7.	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[J]. Pharmacoeconomics, 2010, 28(10): 935-945.
8.	Caro JJ, Ishak KJ. No head-to-head trial. Simulate the missing arms[J]. Pharmacoeconomics, 2010, 28(10): 957-967.
9.	Thokala P, Bujkiewicz S, Ouwens M, et al. Use of MAIC and STC in health technology assessment: a review of the literature[J]. Value Health, 2016, 19(7): A346.
10.	Phillippo DM, Ades AE, Dias S, et al. NICE DSU technical support document 18: methods for population-adjusted indirect comparisons in submissions to NICE[Z]. Sheffield: NICE Decision Support Unit, 2016.
11.	Phillippo DM, Dias S, Ades AE, et al. Multilevel network meta-regression for population-adjusted treatment comparisons[J]. J R Stat Soc Ser A Stat Soc, 2020, 183(3): 1189-1210.
12.	Phillippo DM, Ades AE, Dias S, et al. Methods for population-adjusted indirect comparisons in health technology appraisal[J]. Med Decis Making, 2018, 38(2): 200-211.
13.	Remiro-Azócar A, Heath A, Baio G. Target estimands for population-adjusted indirect comparisons[J]. Stat Med, 2022, 41(28): 5558-5569.
14.	Remiro-Azócar A, Heath A, Baio G. Model-based standardization using multiple imputation[J]. BMC Med Res Methodol, 2024, 24(1): 32.
15.	Berlin JA, Santanna J, Schmid CH, et al. Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head[J]. Stat Med, 2002, 21(3): 371-387.
16.	Remiro-Azócar A. Two-stage matching-adjusted indirect comparison[J]. BMC Med Res Methodol, 2022, 22(1): 217.
17.	Harari O, Soltanifar M, Cappelleri JC, et al. Network meta-interpolation: effect modification adjustment in network meta-analysis using subgroup analyses[J]. Res Synth Methods, 2023, 14(2): 211-233.
18.	Robins JM. A new approach to causal inference in mortality studies with a sustained exposure period - application to control of the healthy worker survivor effect[J]. Math Model, 1986, 7(9-12): 1393-1512.
19.	Snowden JM, Rose S, Mortimer KM. Implementation of G-computation on a simulated data set: demonstration of a causal inference technique[J]. Am J Epidemiol, 2011, 173(7): 731-738.
20.	Hainmueller J. Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies[J]. Polit Anal, 2012, 20(1): 25-46.
21.	Phillippo DM, Dias S, Ades AE, et al. Equivalence of entropy balancing and the method of moments for matching-adjusted indirect comparison[J]. Res Synth Methods, 2020, 11(4): 568-572.
22.	Jackson D, Sewell B, Nones N, et al. Matching-adjusted indirect comparison via a polynomial-based non-linear optimization method[J]. J Comp Eff Res, 2017, 6(4): 307-319.
23.	Cole SR, Hernán MA. Constructing inverse probability weights for marginal structural models[J]. Am J Epidemiol, 2008, 168(6): 656-664.
24.	Faria R, Hernandez-Alava M, Manca A, et al. NICE DSU technical support document 17: the use of observational data to inform estimates of treatment effectiveness in technology appraisal: methods for comparative individual patient data[Z]. Sheffield: NICE Decision Support Unit, 2015.
25.	Remiro-Azócar A, Anand A. Two-stage matching-adjusted indirect comparison: a new standard[J]. Value Health, 2022, 25(7): S292.
26.	Jackson D, White IR, Riley RD. A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression[J]. Biom J, 2013, 55(2): 231-245.
27.	Westreich D, Edwards JK, Lesko CR, et al. Transportability of trial results using inverse odds of sampling weights[J]. Am J Epidemiol, 2017, 186(8): 1010-1014.
28.	Austin PC. Absolute risk reductions and numbers needed to treat can be obtained from adjusted survival models for time-to-event outcomes[J]. J Clin Epidemiol, 2010, 63(1): 46-55.
29.	Chatton A, Le BF, Leyrat C, et al. G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study[J]. Sci Rep, 2020, 10(1): 9219.
30.	Franklin JM, Schneeweiss S. When and how can real world data analyses substitute for randomized controlled trials[J]. Clin Pharmacol Ther, 2017, 102(6): 924-933.
31.	Zohar E, Katz G, Koren G. Approaches to indirect treatment comparison in the absence of a common comparator: a systematic review[J]. Value Health, 2019, 22(5): S380.
32.	Cope S, Zhang J, Salanti G, et al. Population adjustment methods for indirect comparisons: a review of National Institute for Health and Care Excellence technology appraisals[J]. Int J Technol Assess Health Care, 2020, 36(2): 141-149.
33.	Phillippo DM, Dias S, Ades AE, et al. Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis[J]. J R Stat Soc Ser A Stat Soc, 2025, 188(1): 1-24.
34.	Phillippo DM, Dias S, Ades AE, et al. Validating the assumptions of population adjustment: application of multilevel network meta-regression to a network of treatments for plaque psoriasis[J]. Med Decis Making, 2023, 43(1): 53-67.
35.	Wallach JD, Deng Y, Mccoy RG, et al. Characteristics of post hoc subgroup analyses of oncology clinical trials: a systematic review[J]. JNCI Cancer Spectr, 2023, 7(6): pkad100.
36.	Shore N, Khalaf D, Uemura H, et al. Population-adjusted network meta-analyses (NMA) to evaluate the efficacy of treatment alternatives for metastatic hormone-sensitive prostate cancer (mHSPC)[J]. J Clin Oncol, 2025, 43(5_suppl): 267.
37.	Truong B, Tran LAT, Le TA, et al. Population adjusted-indirect comparisons in health technology assessment: a methodological systematic review[J]. Res Synth Methods, 2023, 14(5): 660-670.
38.	Kasner SE, Sondergaard L, Nakum M, et al. A matching-adjusted indirect comparison of results from REDUCE and RESPECT-two randomized trials on patent foramen ovale closure devices to prevent recurrent cryptogenic stroke[J]. J Med Econ, 2024, 27(1): 337-343.
39.	Sylla P, Sagar P, Johnston SS, et al. Outcomes associated with the use of a new powered circular stapler for left-sided colorectal reconstructions: a propensity score matching-adjusted indirect comparison with manual circular staplers[J]. Surg Endosc, 2022, 36(4): 2541-2553.
40.	Chatelet F, Chevret S, Vinciguerra A, et al. Matching-adjusted indirect comparison of endoscopic and craniofacial resection for the treatment of sinonasal cancer invading the skull base[J]. Eur J Cancer, 2025, 220: 115382.
41.	Vachani A, Murgu S, Maldonado F, et al. Comparison of diagnostic bronchoscopy single-arm trials using an advanced optimization approach for indirect comparisons with partial individual patient data[J]. Respiration, 2025, 104(12): 940-949.
42.	Ming J, He Y, Yang Y, et al. Health technology assessment of medical devices: current landscape, challenges, and a way forward[J]. Cost Eff Resour Alloc, 2022, 20(1): 54.
43.	Chen W, Shi L, Li X, et al. Use of health technology assessment in drug reimbursement decisions in China[J]. BMJ, 2023, 381: e074021.
44.	Cheng J, Liu Y, Zhang Y, et al. Matching-adjusted indirect comparison of equecabtagene autoleucel vs. idecabtagene vicleucel for relapsed and refractory multiple myeloma[J]. J Comp Eff Res, 2024, 13(1): e230156.
45.	Li H, Liu Y, Zhang L, et al. Indirect comparison methods in Chinese health technology assessment: a survey of current practice[J]. Value Health Reg Issues, 2021, 24: 12-18.
46.	Tang M, Hu H, He J, et al. Challenges and opportunities for real-world evidence in China's health technology assessment[J]. BMJ Glob Health, 2023, 8(5): e011664.
47.	Canadian Agency for Drugs and Technologies in Health (CADTH). Guidelines for the economic evaluation of health technologies: Canada (4th edition)[Z]. 2017.
48.	Canada's Drug Agency. Methods guide for health technology assessment[Z]. Ottawa: Canada's Drug Agency, 2025.
49.	HTA Coordination Group. Methodological guideline for quantitative evidence synthesis: direct and indirect comparisons[Z]. Brussels: European Commission, 2024.
50.	HTA Coordination Group. Practical guideline for quantitative evidence synthesis: direct and indirect comparisons[Z]. Brussels: European Commission, 2024.
51.	Nevitt S, Phillippo DM, Hodgson R, et al. Application of multi-level network meta-regression in the NICE technology appraisal of quizartinib for induction, consolidation and maintenance treatment of newly diagnosed FLT3-ITD-positive acute myeloid leukaemia: an external assessment group perspective[J]. Pharmacoeconomics, 2025, 43(3): 243-247.
52.	National Institute for Health and Care Excellence. Quizartinib for induction, consolidation and maintenance treatment of newly diagnosed FLT3-ITD-positive acute myeloid leukaemia: committee discussion (TA1013)[Z]. 2024.
53.	National Institute for Health and Care Excellence. Durvalumab with chemotherapy before surgery and durvalumab after surgery for resectable non-small-cell lung cancer: committee discussion (TA1030)[Z]. 2025.
54.	Institute for Clinical and Economic Review (ICER). 2020-2023 value assessment framework[Z]. 2020.
55.	National Institute for Health and Care Excellence. Tarlatamab for extensive-stage small-cell lung cancer after 2 or more treatments: committee discussion (TA1091)[Z]. 2025.
56.	National Institute for Health and Care Excellence. Amivantamab with lazertinib for untreated EGFR mutation-positive advanced non-small-cell lung cancer: committee discussion (TA1122)[Z]. 2026.
57.	National Institute for Health and Care Excellence. Zanubrutinib for treating marginal zone lymphoma after anti-CD20-based treatment: committee discussion (TA1001)[Z]. 2024.
58.	European Network for Health Technology Assessment (EUnetHTA). Methodological guideline D4.3. 1 - Direct and indirect comparison[Z]. Diemen: EUnetHTA, 2020.
59.	劉國恩. 中國藥物經濟學評價指南(2020中英雙語版). 北京: 中國市場出版社, 2020.

1. Bucher HC, Guyatt GH, Griffith LE, et al. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials[J]. J Clin Epidemiol, 1997, 50(6): 683-691.
2. Wells GA, Sultan SA, Chen L, et al. Indirect evidence: indirect treatment comparisons in meta-analysis[Z]. Ottawa: Canadian Agency for Drugs and Technologies in Health, 2009.
3. 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[J]. Res Synth Methods, 2012, 3(2): 80-97.
4. Jansen JP, Naci H. Is network meta-analysis as valid as standard pairwise meta-analysis. It all depends on the distribution of effect modifiers[J]. BMC Med, 2013, 11: 159.
5. Cipriani A, Higgins JP, Geddes JR, et al. Conceptual and technical challenges in network meta-analysis[J]. Ann Intern Med, 2013, 159(2): 130-137.
6. Song F, Harvey I, Lilford R. Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions[J]. J Clin Epidemiol, 2008, 61(5): 455-463.
7. 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[J]. Pharmacoeconomics, 2010, 28(10): 935-945.
8. Caro JJ, Ishak KJ. No head-to-head trial. Simulate the missing arms[J]. Pharmacoeconomics, 2010, 28(10): 957-967.
9. Thokala P, Bujkiewicz S, Ouwens M, et al. Use of MAIC and STC in health technology assessment: a review of the literature[J]. Value Health, 2016, 19(7): A346.
10. Phillippo DM, Ades AE, Dias S, et al. NICE DSU technical support document 18: methods for population-adjusted indirect comparisons in submissions to NICE[Z]. Sheffield: NICE Decision Support Unit, 2016.
11. Phillippo DM, Dias S, Ades AE, et al. Multilevel network meta-regression for population-adjusted treatment comparisons[J]. J R Stat Soc Ser A Stat Soc, 2020, 183(3): 1189-1210.
12. Phillippo DM, Ades AE, Dias S, et al. Methods for population-adjusted indirect comparisons in health technology appraisal[J]. Med Decis Making, 2018, 38(2): 200-211.
13. Remiro-Azócar A, Heath A, Baio G. Target estimands for population-adjusted indirect comparisons[J]. Stat Med, 2022, 41(28): 5558-5569.
14. Remiro-Azócar A, Heath A, Baio G. Model-based standardization using multiple imputation[J]. BMC Med Res Methodol, 2024, 24(1): 32.
15. Berlin JA, Santanna J, Schmid CH, et al. Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head[J]. Stat Med, 2002, 21(3): 371-387.
16. Remiro-Azócar A. Two-stage matching-adjusted indirect comparison[J]. BMC Med Res Methodol, 2022, 22(1): 217.
17. Harari O, Soltanifar M, Cappelleri JC, et al. Network meta-interpolation: effect modification adjustment in network meta-analysis using subgroup analyses[J]. Res Synth Methods, 2023, 14(2): 211-233.
18. Robins JM. A new approach to causal inference in mortality studies with a sustained exposure period - application to control of the healthy worker survivor effect[J]. Math Model, 1986, 7(9-12): 1393-1512.
19. Snowden JM, Rose S, Mortimer KM. Implementation of G-computation on a simulated data set: demonstration of a causal inference technique[J]. Am J Epidemiol, 2011, 173(7): 731-738.
20. Hainmueller J. Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies[J]. Polit Anal, 2012, 20(1): 25-46.
21. Phillippo DM, Dias S, Ades AE, et al. Equivalence of entropy balancing and the method of moments for matching-adjusted indirect comparison[J]. Res Synth Methods, 2020, 11(4): 568-572.
22. Jackson D, Sewell B, Nones N, et al. Matching-adjusted indirect comparison via a polynomial-based non-linear optimization method[J]. J Comp Eff Res, 2017, 6(4): 307-319.
23. Cole SR, Hernán MA. Constructing inverse probability weights for marginal structural models[J]. Am J Epidemiol, 2008, 168(6): 656-664.
24. Faria R, Hernandez-Alava M, Manca A, et al. NICE DSU technical support document 17: the use of observational data to inform estimates of treatment effectiveness in technology appraisal: methods for comparative individual patient data[Z]. Sheffield: NICE Decision Support Unit, 2015.
25. Remiro-Azócar A, Anand A. Two-stage matching-adjusted indirect comparison: a new standard[J]. Value Health, 2022, 25(7): S292.
26. Jackson D, White IR, Riley RD. A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression[J]. Biom J, 2013, 55(2): 231-245.
27. Westreich D, Edwards JK, Lesko CR, et al. Transportability of trial results using inverse odds of sampling weights[J]. Am J Epidemiol, 2017, 186(8): 1010-1014.
28. Austin PC. Absolute risk reductions and numbers needed to treat can be obtained from adjusted survival models for time-to-event outcomes[J]. J Clin Epidemiol, 2010, 63(1): 46-55.
29. Chatton A, Le BF, Leyrat C, et al. G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study[J]. Sci Rep, 2020, 10(1): 9219.
30. Franklin JM, Schneeweiss S. When and how can real world data analyses substitute for randomized controlled trials[J]. Clin Pharmacol Ther, 2017, 102(6): 924-933.
31. Zohar E, Katz G, Koren G. Approaches to indirect treatment comparison in the absence of a common comparator: a systematic review[J]. Value Health, 2019, 22(5): S380.
32. Cope S, Zhang J, Salanti G, et al. Population adjustment methods for indirect comparisons: a review of National Institute for Health and Care Excellence technology appraisals[J]. Int J Technol Assess Health Care, 2020, 36(2): 141-149.
33. Phillippo DM, Dias S, Ades AE, et al. Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis[J]. J R Stat Soc Ser A Stat Soc, 2025, 188(1): 1-24.
34. Phillippo DM, Dias S, Ades AE, et al. Validating the assumptions of population adjustment: application of multilevel network meta-regression to a network of treatments for plaque psoriasis[J]. Med Decis Making, 2023, 43(1): 53-67.
35. Wallach JD, Deng Y, Mccoy RG, et al. Characteristics of post hoc subgroup analyses of oncology clinical trials: a systematic review[J]. JNCI Cancer Spectr, 2023, 7(6): pkad100.
36. Shore N, Khalaf D, Uemura H, et al. Population-adjusted network meta-analyses (NMA) to evaluate the efficacy of treatment alternatives for metastatic hormone-sensitive prostate cancer (mHSPC)[J]. J Clin Oncol, 2025, 43(5_suppl): 267.
37. Truong B, Tran LAT, Le TA, et al. Population adjusted-indirect comparisons in health technology assessment: a methodological systematic review[J]. Res Synth Methods, 2023, 14(5): 660-670.
38. Kasner SE, Sondergaard L, Nakum M, et al. A matching-adjusted indirect comparison of results from REDUCE and RESPECT-two randomized trials on patent foramen ovale closure devices to prevent recurrent cryptogenic stroke[J]. J Med Econ, 2024, 27(1): 337-343.
39. Sylla P, Sagar P, Johnston SS, et al. Outcomes associated with the use of a new powered circular stapler for left-sided colorectal reconstructions: a propensity score matching-adjusted indirect comparison with manual circular staplers[J]. Surg Endosc, 2022, 36(4): 2541-2553.
40. Chatelet F, Chevret S, Vinciguerra A, et al. Matching-adjusted indirect comparison of endoscopic and craniofacial resection for the treatment of sinonasal cancer invading the skull base[J]. Eur J Cancer, 2025, 220: 115382.
41. Vachani A, Murgu S, Maldonado F, et al. Comparison of diagnostic bronchoscopy single-arm trials using an advanced optimization approach for indirect comparisons with partial individual patient data[J]. Respiration, 2025, 104(12): 940-949.
42. Ming J, He Y, Yang Y, et al. Health technology assessment of medical devices: current landscape, challenges, and a way forward[J]. Cost Eff Resour Alloc, 2022, 20(1): 54.
43. Chen W, Shi L, Li X, et al. Use of health technology assessment in drug reimbursement decisions in China[J]. BMJ, 2023, 381: e074021.
44. Cheng J, Liu Y, Zhang Y, et al. Matching-adjusted indirect comparison of equecabtagene autoleucel vs. idecabtagene vicleucel for relapsed and refractory multiple myeloma[J]. J Comp Eff Res, 2024, 13(1): e230156.
45. Li H, Liu Y, Zhang L, et al. Indirect comparison methods in Chinese health technology assessment: a survey of current practice[J]. Value Health Reg Issues, 2021, 24: 12-18.
46. Tang M, Hu H, He J, et al. Challenges and opportunities for real-world evidence in China's health technology assessment[J]. BMJ Glob Health, 2023, 8(5): e011664.
47. Canadian Agency for Drugs and Technologies in Health (CADTH). Guidelines for the economic evaluation of health technologies: Canada (4th edition)[Z]. 2017.
48. Canada's Drug Agency. Methods guide for health technology assessment[Z]. Ottawa: Canada's Drug Agency, 2025.
49. HTA Coordination Group. Methodological guideline for quantitative evidence synthesis: direct and indirect comparisons[Z]. Brussels: European Commission, 2024.
50. HTA Coordination Group. Practical guideline for quantitative evidence synthesis: direct and indirect comparisons[Z]. Brussels: European Commission, 2024.
51. Nevitt S, Phillippo DM, Hodgson R, et al. Application of multi-level network meta-regression in the NICE technology appraisal of quizartinib for induction, consolidation and maintenance treatment of newly diagnosed FLT3-ITD-positive acute myeloid leukaemia: an external assessment group perspective[J]. Pharmacoeconomics, 2025, 43(3): 243-247.
52. National Institute for Health and Care Excellence. Quizartinib for induction, consolidation and maintenance treatment of newly diagnosed FLT3-ITD-positive acute myeloid leukaemia: committee discussion (TA1013)[Z]. 2024.
53. National Institute for Health and Care Excellence. Durvalumab with chemotherapy before surgery and durvalumab after surgery for resectable non-small-cell lung cancer: committee discussion (TA1030)[Z]. 2025.
54. Institute for Clinical and Economic Review (ICER). 2020-2023 value assessment framework[Z]. 2020.
55. National Institute for Health and Care Excellence. Tarlatamab for extensive-stage small-cell lung cancer after 2 or more treatments: committee discussion (TA1091)[Z]. 2025.
56. National Institute for Health and Care Excellence. Amivantamab with lazertinib for untreated EGFR mutation-positive advanced non-small-cell lung cancer: committee discussion (TA1122)[Z]. 2026.
57. National Institute for Health and Care Excellence. Zanubrutinib for treating marginal zone lymphoma after anti-CD20-based treatment: committee discussion (TA1001)[Z]. 2024.
58. European Network for Health Technology Assessment (EUnetHTA). Methodological guideline D4.3. 1 - Direct and indirect comparison[Z]. Diemen: EUnetHTA, 2020.
59. 劉國恩. 中國藥物經濟學評價指南(2020中英雙語版). 北京: 中國市場出版社, 2020.

Chinese Journal of Evidence-Based Medicine

Progress of next-generation population-adjusted indirect comparison methods

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content