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：

Export PDF Favorites Scan Get Citation

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

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.	陳新林, 胡月, 郎建英, 等. RevMan, Stata和R軟件在生存資料Meta分析的應用. 中國循證醫學雜志, 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.	張天嵩, 董圣杰, 楊智榮, 等. 網絡Meta分析研究進展系列(十三): 生存數據的網絡Meta分析. 中國循證心血管醫學雜志, 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.	石豐豪, 商葉, 芮明軍, 等. R語言survHE程序包在衛生經濟學評價中的應用. 中國衛生經濟, 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.

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. 陳新林, 胡月, 郎建英, 等. RevMan, Stata和R軟件在生存資料Meta分析的應用. 中國循證醫學雜志, 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. 張天嵩, 董圣杰, 楊智榮, 等. 網絡Meta分析研究進展系列(十三): 生存數據的網絡Meta分析. 中國循證心血管醫學雜志, 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. 石豐豪, 商葉, 芮明軍, 等. R語言survHE程序包在衛生經濟學評價中的應用. 中國衛生經濟, 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

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content