Introduction and application of network meta-interpolation_Chinese Journal of Evidence-Based Medicine

Authors：

SUN Lingyao ^1,2 , ZHOU Dachuang ^1,2 , HU Hongfei ^1,2 ,  TIAN Lei ^1,2

1. International Pharmaceutical Business School, China Pharmaceutical University, Nanjing 211198, P. R. China;
2. Pharmacoeconomic Evaluation Research Center, China Pharmaceutical University, Nanjing 211198, P. R. China;

Corresponding?author：

TIAN Lei, Email: cputianlei@163.com

Keywords：

Network meta interpolation; Shared effect modification; Effect comparison; Meta-analysis

DOI：

10.7507/1672-2531.202509141

Video：

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Abstract Full text Figures/Tables Video References Cited by

In health technology assessment, existing covariate adjustment methods-such as network meta regression, matching-adjusted indirect comparison, and simulated treatment comparison often implicitly rely on the shared effect modification (SEM) assumption. When this assumption does not hold, systematic bias may arise. Network meta interpolation (NMI), by contrast, does not depend on the SEM assumption. Instead, it leverages the correlation structure of covariates, applying best linear unbiased prediction to impute unreported subgroup data, which are then used for regression analysis to obtain indirect comparison results. In this study, we conducted Monte Carlo simulations to evaluate the performance of NMI and network meta-analysis across three scenarios. The results showed that, regardless of whether the SEM assumption held, NMI produced estimates that were closer to the true values and confidence intervals with higher coverage. This method therefore offers an important complement for addressing covariate imbalance and non-shared effect modification in HTA, though caution is warranted in interpreting results due to the risks of multiple comparisons and inflated type I error.

Citation： SUN Lingyao, ZHOU Dachuang, HU Hongfei, TIAN Lei. Introduction and application of network meta-interpolation. Chinese Journal of Evidence-Based Medicine, 2026, 26(4): 474-478. doi: 10.7507/1672-2531.202509141 Copy

1.	Salanti G, Higgins JP, Ades AE, et al. Evaluation of networks of randomized trials. Stat Methods Med Res, 2008, 17(3): 279-301.
2.	Dias S, Sutton AJ, Ades AE, et al. Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Making, 2013, 33(5): 607-617.
3.	Tonin FS, Rotta I, Mendes AM, et al. Network meta-analysis: a technique to gather evidence from direct and indirect comparisons. Pharm Pract (Granada), 2017, 15(1): 943.
4.	Baker WL, White CM, Cappelleri JC, et al. Understanding heterogeneity in meta-analysis: the role of meta-regression. Int J Clin Pract, 2009, 63(10): 1426-1434.
5.	Signorovitch JE, Sikirica V, Erder MH, et al. Matching-adjusted indirect comparisons: a new tool for timely comparative effectiveness research. Value Health, 2012, 15(6): 940-947.
6.	Ishak KJ, Proskorovsky I, Benedict A. Simulation and matching-based approaches for indirect comparison of treatments. Pharmacoeconomics, 2015, 33(6): 537-549.
7.	Phillippo DM, Ades AE, Dias S, et al. Methods for population-adjusted indirect comparisons in health technology appraisal. Med Decis Making, 2018, 38(2): 200-211.
8.	許佳藝, 代展菁, 盧鈺瓊, 等. 匹配調整間接比較應用的方法學要點和實踐現狀分析. 中國循證醫學雜志, 2024, 24(3): 322-330.
9.	Harari O, Soltanifar M, Cappelleri JC, et al. Network meta-interpolation: effect modification adjustment in network meta-analysis using subgroup analyses. Res Synth Methods, 2023, 14(2): 211-233.
10.	Jansen JP, Naci H. Is network meta-analysis as valid as standard pairwise meta-analysis. It all depends on the distribution of effect modifiers. BMC Med, 2013, 11: 159.
11.	Riley RD, Dias S, Donegan S, et al. Using individual participant data to improve network meta-analysis projects. BMJ Evid Based Med, 2023, 28(3): 197-203.
12.	Macabeo B, Quenéchdu A, Aballéa S, et al. Methods for indirect treatment comparison: results from a systematic literature review. J Mark Access Health Policy, 2024, 12(2): 58-80.
13.	Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics, 1975, 31(2): 423.
14.	Banack HR, Hayes-Larson E, Mayeda ER. Monte Carlo simulation approaches for quantitative bias analysis: a tutorial. Epidemiol Rev, 2022, 43(1): 106-117.
15.	Wheaton L, Gsteiger S, Hubbard S, et al. Combining treatment effects from mixed populations in meta-analysis: a review of methods. BMC Med Res Methodol, 2025, 25(1): 86.
16.	Freitag A, Gurskyte L, Sarri G. Increasing transparency in indirect treatment comparisons: is selecting effect modifiers the missing part of the puzzle. A review of methodological approaches and critical considerations. J Comp Eff Res, 2023, 12(10): e230046.
17.	James M. The generalised inverse. Math Gaz, 1978, 62(420): 109-114.
18.	Li G, Taljaard M, Van Den Heuvel ER, et al. An introduction to multiplicity issues in clinical trials: the what, why, when and how. Int J Epidemiol, 2017, 46(2): 746-755.
19.	Fingerhut A, Uranues S, Dziri C, et al. Interaction analysis of subgroup effects in randomized trials: the essential methodological points. Sci Rep, 2024, 14(1): 12619.

1. Salanti G, Higgins JP, Ades AE, et al. Evaluation of networks of randomized trials. Stat Methods Med Res, 2008, 17(3): 279-301.
2. Dias S, Sutton AJ, Ades AE, et al. Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med Decis Making, 2013, 33(5): 607-617.
3. Tonin FS, Rotta I, Mendes AM, et al. Network meta-analysis: a technique to gather evidence from direct and indirect comparisons. Pharm Pract (Granada), 2017, 15(1): 943.
4. Baker WL, White CM, Cappelleri JC, et al. Understanding heterogeneity in meta-analysis: the role of meta-regression. Int J Clin Pract, 2009, 63(10): 1426-1434.
5. Signorovitch JE, Sikirica V, Erder MH, et al. Matching-adjusted indirect comparisons: a new tool for timely comparative effectiveness research. Value Health, 2012, 15(6): 940-947.
6. Ishak KJ, Proskorovsky I, Benedict A. Simulation and matching-based approaches for indirect comparison of treatments. Pharmacoeconomics, 2015, 33(6): 537-549.
7. Phillippo DM, Ades AE, Dias S, et al. Methods for population-adjusted indirect comparisons in health technology appraisal. Med Decis Making, 2018, 38(2): 200-211.
8. 許佳藝, 代展菁, 盧鈺瓊, 等. 匹配調整間接比較應用的方法學要點和實踐現狀分析. 中國循證醫學雜志, 2024, 24(3): 322-330.
9. Harari O, Soltanifar M, Cappelleri JC, et al. Network meta-interpolation: effect modification adjustment in network meta-analysis using subgroup analyses. Res Synth Methods, 2023, 14(2): 211-233.
10. Jansen JP, Naci H. Is network meta-analysis as valid as standard pairwise meta-analysis. It all depends on the distribution of effect modifiers. BMC Med, 2013, 11: 159.
11. Riley RD, Dias S, Donegan S, et al. Using individual participant data to improve network meta-analysis projects. BMJ Evid Based Med, 2023, 28(3): 197-203.
12. Macabeo B, Quenéchdu A, Aballéa S, et al. Methods for indirect treatment comparison: results from a systematic literature review. J Mark Access Health Policy, 2024, 12(2): 58-80.
13. Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics, 1975, 31(2): 423.
14. Banack HR, Hayes-Larson E, Mayeda ER. Monte Carlo simulation approaches for quantitative bias analysis: a tutorial. Epidemiol Rev, 2022, 43(1): 106-117.
15. Wheaton L, Gsteiger S, Hubbard S, et al. Combining treatment effects from mixed populations in meta-analysis: a review of methods. BMC Med Res Methodol, 2025, 25(1): 86.
16. Freitag A, Gurskyte L, Sarri G. Increasing transparency in indirect treatment comparisons: is selecting effect modifiers the missing part of the puzzle. A review of methodological approaches and critical considerations. J Comp Eff Res, 2023, 12(10): e230046.
17. James M. The generalised inverse. Math Gaz, 1978, 62(420): 109-114.
18. Li G, Taljaard M, Van Den Heuvel ER, et al. An introduction to multiplicity issues in clinical trials: the what, why, when and how. Int J Epidemiol, 2017, 46(2): 746-755.
19. Fingerhut A, Uranues S, Dziri C, et al. Interaction analysis of subgroup effects in randomized trials: the essential methodological points. Sci Rep, 2024, 14(1): 12619.

Chinese Journal of Evidence-Based Medicine

Introduction and application of network meta-interpolation

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