Non-constant proportional hazards network meta-analysis: a case study in R software_Chinese Journal of Evidence-Based Medicine

Authors：

ZHAO Mingye ^1,2 , SHAO Taihang ^1,2 ,  TANG Wenxi ^1,2

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

Corresponding author：

TANG Wenxi, Email: tokammy@cpu.edu.cn

Keywords：

Non-constant hazard ratio; Fractional polynomial; Network meta-analysis; Survival data

DOI：

10.7507/1672-2531.202202001

Video：

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Abstract Full text Figures/Tables Video References Cited by

Network meta-analyses (NMA) of survival data often rely on the proportional hazards (PH) assumption, however, this assumption fails when survival curves intersect. With the emergence of innovative therapies such as immunotherapy, the importance of NMA based on non-proportional hazards (non-PH) in the current evidence-based medicine evaluation of oncology drugs has become increasingly prominent. Fractional polynomial (FP) models do not rely on the assumption of PH, which can flexibly capture the characteristics of survival curves, and the corresponding fitting effects are better than those of the PH models. This study introduced a complete workflow in R for NMA using FP models with non-PH.

Citation： ZHAO Mingye, SHAO Taihang, TANG Wenxi. Non-constant proportional hazards network meta-analysis: a case study in R software. Chinese Journal of Evidence-Based Medicine, 2022, 22(7): 853-861. doi: 10.7507/1672-2531.202202001 Copy

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23.	石丰豪, 商叶, 芮明军, 等. R语言survHE程序包在卫生经济学评价中的应用. 中国卫生经济, 2020, 39(9): 9-14.
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25.	Vickers AD, Winfree KB, Cuyun Carter G, et al. Relative efficacy of interventions in the treatment of second-line non-small cell lung cancer: a systematic review and network meta-analysis. BMC Cancer, 2019, 19(1): 353.

1. Jansen JP, Fleurence R, Devine B, et al. Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR task force on indirect treatment comparisons good research practices: part 1. Value Health, 2011, 14(4): 417-428.
2. 陈新林, 胡月, 郎建英, 等. RevMan, Stata和R软件在生存资料Meta分析的应用. 中国循证医学杂志, 2016, 16(6): 736-740.
3. Heo JH, Park C, Ghosh S, et al. A network meta-analysis of efficacy and safety of first-line and second-line therapies for the management of metastatic renal cell carcinoma. J Clin Pharm Ther, 2021, 46(1): 35-49.
4. Cranmer HL, Harvey RC. Network meta-analysis of hazard ratios vs. fractional polynomials approach in a cost-effectiveness analysis considering advanced gastric cancer. Value Health, 2017, 20(9): A401.
5. Anagnostou V, Yarchoan M, Hansen AR, et al. Immuno-oncology trial endpoints: capturing clinically meaningful activity. Clin Cancer Res, 2017, 23(17): 4959-4969.
6. Chen TT. Statistical issues and challenges in immuno-oncology. J Immunother Cancer, 2013, 1: 18.
7. Ouwens MJ, Philips Z, Jansen JP. Network meta-analysis of parametric survival curves. Res Synth Methods, 2010, 1(3-4): 258-271.
8. Lu G, Ades AE, Sutton AJ, et al. Meta-analysis of mixed treatment comparisons at multiple follow-up times. Stat Med, 2007, 26(20): 3681-3699.
9. Petit C, Blanchard P, Pignon JP, et al. Individual patient data network meta-analysis using either restricted mean survival time difference or hazard ratios: is there a difference. A case study on locoregionally advanced nasopharyngeal carcinomas. Syst Rev, 2019, 8(1): 96.
10. Jansen JP. Network meta-analysis of survival data with fractional polynomials. BMC Med Res Methodol, 2011, 11: 61.
11. Abrams KR. CHTE2020 Sources and synthesis of evidence, 2020. Available at:https://nicedsu.sites.sheffield.ac.uk/.
12. 张天嵩, 董圣杰, 杨智荣, 等. 网络Meta分析研究进展系列(十三): 生存数据的网络Meta分析. 中国循证心血管医学杂志, 2021, 13(6): 649-652.
13. Prentice RL, Gloeckler LA. Regression analysis of grouped survival data with application to breast cancer data. Biometrics, 1978, 34(1): 57-67.
14. Wiksten A, Hawkins N, Piepho HP, et al. Nonproportional hazards in network meta-analysis: efficient strategies for model building and analysis. Value Health, 2020, 23(7): 918-927.
15. Chang A, Parikh P, Thongprasert S, et al. Gefitinib (IRESSA) in patients of Asian origin with refractory advanced non-small cell lung cancer: subset analysis from the ISEL study. J Thorac Oncol, 2006, 1(8): 847-855.
16. Cufer T, Vrdoljak E, Gaafar R, et al. Phase Ⅱ, open-label, randomized study (SIGN) of single-agent gefitinib (IRESSA) or docetaxel as second-line therapy in patients with advanced (stage Ⅲb or Ⅳ) non-small-cell lung cancer. Anticancer Drugs, 2006, 17(4): 401-409.
17. Hanna N, Shepherd FA, Fossella FV, et al. Randomized phase Ⅲ trial of pemetrexed versus docetaxel in patients with non-small-cell lung cancer previously treated with chemotherapy. J Clin Oncol, 2004, 22(9): 1589-1597.
18. Kim ES, Hirsh V, Mok T, et al. Gefitinib versus docetaxel in previously treated non-small-cell lung cancer (INTEREST): a randomised phase Ⅲ trial. Lancet, 2008, 372(9652): 1809-1818.
19. Lee DH, Park K, Kim JH, et al. Randomized phase Ⅲ trial of gefitinib versus docetaxel in non-small cell lung cancer patients who have previously received platinum-based chemotherapy. Clin Cancer Res, 2010, 16(4): 1307-1314.
20. Maruyama R, Nishiwaki Y, Tamura T, et al. Phase Ⅲ study, V-15-32, of gefitinib versus docetaxel in previously treated Japanese patients with non–small-cell lung cancer. J Clin Oncol, 2008, 26(26): 4244-4252.
21. Shepherd FA, Dancey J, Ramlau R, et al. Prospective randomized trial of docetaxel versus best supportive care in patients with non-small-cell lung cancer previously treated with platinum-based chemotherapy. J Clin Oncol, 2000, 18(10): 2095-2103.
22. Guyot P, Ades AE, Ouwens MJ, et al. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res Methodol, 2012, 12: 9.
23. 石丰豪, 商叶, 芮明军, 等. R语言survHE程序包在卫生经济学评价中的应用. 中国卫生经济, 2020, 39(9): 9-14.
24. Wang L, Paller CJ, Hong H, et al. Comparison of systemic treatments for metastatic castration-sensitive prostate cancer: a systematic review and network meta-analysis. JAMA Oncol, 2021, 7(3): 412-420.
25. Vickers AD, Winfree KB, Cuyun Carter G, et al. Relative efficacy of interventions in the treatment of second-line non-small cell lung cancer: a systematic review and network meta-analysis. BMC Cancer, 2019, 19(1): 353.

Chinese Journal of Evidence-Based Medicine

Non-constant proportional hazards network meta-analysis: a case study in R software

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