Skip to main content
Search
Search
Advanced Search
Skip main navigation
Open access
Meta-Analysis
11 July 2023

Challenges in conducting fractional polynomial and standard parametric network meta-analyses of immune checkpoint inhibitors for first-line advanced renal cell carcinoma

Authors: Svenja Petersohn https://orcid.org/0000-0001-5058-7645, Bradley McGregor, Sven L Klijn, Jessica R May, Flavia Ejzykowicz, Murat Kurt, Matthew Dyer, Bill Malcolm, Sébastien Branchoux, Katharina Nickel, Saby George, and Sonja Kroep [email protected]Author Info & Affiliations
Volume 12, Number 8
https://doi.org/10.57264/cer-2023-0004
PDFFormats

Abstract

Aim: Network meta-analyses (NMAs) increasingly feature time-varying hazards to account for non-proportional hazards between different drug classes. This paper outlines an algorithm for selecting clinically plausible fractional polynomial NMA models. Methods: The NMA of four immune checkpoint inhibitors (ICIs) + tyrosine kinase inhibitors (TKIs) and one TKI therapy for renal cell carcinoma (RCC) served as case study. Overall survival (OS) and progression free survival (PFS) data were reconstructed from the literature, 46 models were fitted. The algorithm entailed a-priori face validity criteria for survival and hazards, based on clinical expert input, and predictive accuracy against trial data. Selected models were compared with statistically best-fitting models. Results: Three valid PFS and two OS models were identified. All models overestimated PFS, the OS model featured crossing ICI + TKI versus TKI curves as per expert opinion. Conventionally selected models showed implausible survival. Conclusion: The selection algorithm considering face validity, predictive accuracy, and expert opinion improved the clinical plausibility of first-line RCC survival models.

Plain language summary

What was the aim of this article?

This article demonstrates an algorithm for selecting statistical models for network meta-analyses where the treatment effect varies over time. The algorithm is focused on selecting models that are closely aligned with what is seen in clinical practice.

How was this research carried out?

A network meta-analysis of four combinations of immune checkpoint inhibitors and tyrosine kinase inhibitors, and one single tyrosine kinase inhibitor for renal cell carcinoma was carried out, and 46 models were assessed. Two clinical experts were consulted separately and presented with the models to define clinical validity criteria. The plausibility was compared between models selected using expert opinion and models selected based on statistical fit.

What were the results?

The algorithm improved clinical plausibility and validity of the modelled survival, and the fit of the models with trial data. In case of overall survival, models selected purely on statistical characteristics fit poorly to the data from the start of the trial, whereas the algorithm-selected model showed better fit to the whole trial. However, challenges in the analysis of the treatments remained, since there was considerable heterogeneity across the characteristics of the included trials, which can lead to bias in the analysis.

What do these results mean?

Previous studies have not used this approach of consulting clinicians, and have solely used statistical methods for model selection. Therefore, the new model selection algorithm is a step forward compared with current practice. Further research is warranted on the evolving methods used for network meta-analyses.

Supplementary Material

File (supplementary material.docx)

References

1.
Escudier B, Porta C, Schmidinger M et al. Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-updagger. Ann. Oncol. 30(5), 706–720 (2019).
PubMed
Google Scholar
2.
Rassy E, Flippot R, Albiges L. Tyrosine kinase inhibitors and immunotherapy combinations in renal cell carcinoma. Ther. Adv. Med. Oncol. 12, (2020).
Crossref
PubMed
Google Scholar
5.
FDA. FDA approves pembrolizumab plus axitinib for advanced renal cell carcinoma (2019). www.fda.gov/drugs/drug-approvals-and-databases/fda-approves-pembrolizumab-plus-axitinib-advanced-renal-cell-carcinoma
Google Scholar
7.
Hahn AW, Klaassen Z, Agarwal N et al. First-line treatment of metastatic renal cell carcinoma: a systematic review and network meta-analysis. Eur. Urol. Oncol. 2(6), 708–715 (2019).
PubMed
Google Scholar
8.
Riaz IB, He H, Ryu AJ et al. A Living, Interactive systematic review and network meta-analysis of first-line treatment of metastatic renal cell carcinoma. Eur. Urol. 80(6), 712–723 (2021).
PubMed
Google Scholar
9.
Lombardi P, Filetti M, Falcone R et al. New first-line immunotherapy-based combinations for metastatic renal cell carcinoma: a systematic review and network meta-analysis. Cancer Treat. Rev. 106, (2022).
Crossref
PubMed
Google Scholar
10.
Qu H, Mu Z, Wang K, Hu B. A network meta-analysis of the differences in effectiveness and safety between nivolumab and targeted drug therapy in metastatic renal cell carcinoma. J. Oncol. 2022, (2022).
Crossref
Google Scholar
11.
Bhatnagar N, Lakshmi PV, Jeyashree K. Multiple treatment and indirect treatment comparisons: an overview of network meta-analysis. Perspect. Clin. Res. 5(4), 154–158 (2014).
PubMed
Google Scholar
12.
Monnickendam G, Zhu M, Mckendrick J, Su Y. Measuring survival benefit in health technology assessment in the presence of nonproportional hazards. Value Health 22(4), 431–438 (2019).
PubMed
Google Scholar
13.
Freeman SC, Cooper NJ, Sutton AJ, Crowther MJ, Carpenter JR, Hawkins N. Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: application to a melanoma network. Stat. Methods Med. Res. 31(5), 839–861 (2022).
PubMed
Google Scholar
14.
Freeman SC, Sutton AJ, Cooper NJ. Uptake of methodological advances for synthesis of continuous and time-to-event outcomes would maximize use of the evidence base. J. Clin. Epidemiol. 124, 94–105 (2020).
PubMed
Google Scholar
15.
Jansen JP. Network meta-analysis of survival data with fractional polynomials. BMC Med. Res. Methodol. 11, 61 (2011).
PubMed
Google Scholar
16.
Ouwens MJ, Philips Z, Jansen JP. Network meta-analysis of parametric survival curves. Res. Synth. Methods 1(3–4), 258–271 (2010).
PubMed
Google Scholar
17.
NICE. Avelumab with axitinib for untreated advanced or metastatic renal cell carcinoma (2019). www.nice.org.uk/guidance/ta645/evidence
Google Scholar
18.
HAS. OPDIVO 10mg/ml (nivolumab) - carcinome à cellules rénales avancé, en association cabozantinib (2021). www.has-sante.fr/jcms/p_3297501/fr/opdivo-10-mg/ml-nivolumab-carcinome-a-cellules-renales-avance-en-association-cabozantinib
Google Scholar
19.
Royston P, Altman DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. Appl. Stat. 43(3), 429–467 (1994).
Google Scholar
20.
Elaidi R, Phan L, Borchiellini D et al. Comparative efficacy of first-line immune-based combination therapies in metastatic renal cell carcinoma: a systematic review and network meta-analysis. Cancers 12(6), 1673 (2020).
PubMed
Google Scholar
21.
Mihajlovic J, Postma MJ. Network meta-analysis of survival data using fractional polynomials - an example with first line metastatic renal cell cancer treatments. Value Health 18(7), A343 (2015).
Google Scholar
22.
McGregor BA, Petersohn S, Klijn S et al. Network meta-analysis (NMA) of first-line advanced renal cell carcinoma (1L aRCC) treatments: development of a decision algorithm for fractional polynomial (FP) model selection. J. Clin. Oncol. 40(Suppl. 6), 391–391 (2022).
Google Scholar
23.
Kraan CW, Nientker L, May J et al. PCN21 efficacy and safety in previously untreated, advanced/metastatic renal cell carcinoma – a systematic literature review update. Value Health 23, S424 (2020).
Google Scholar
24.
Heng DY, Xie W, Regan MM et al. Prognostic factors for overall survival in patients with metastatic renal cell carcinoma treated with vascular endothelial growth factor-targeted agents: results from a large, multicenter study. J. Clin. Oncol. 27(34), 5794–5799 (2009).
PubMed
Google Scholar
26.
Guyot P, Ades AE, Ouwens MJ, Welton NJ. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med. Res. Methodol. 12, 9 (2012).
PubMed
Google Scholar
27.
Motzer R, Alekseev B, Rha SY et al. Lenvatinib plus pembrolizumab or everolimus for advanced renal cell carcinoma. N. Engl. J. Med. 384(14), 1289–1300 (2021).
PubMed
Google Scholar
28.
Rini BI, Plimack ER, Stus V et al. Pembrolizumab (pembro) plus axitinib (axi) versus sunitinib as first-line therapy for advanced clear cell renal cell carcinoma (ccRCC): results from 42-month follow-up of KEYNOTE-426. J. Clin. Oncol. 39(Suppl. 15), 4500–4500 (2021).
Google Scholar
29.
Choueiri TK, Motzer RJ, Rini BI et al. Updated efficacy results from the JAVELIN renal 101 trial: first-line avelumab plus axitinib versus sunitinib in patients with advanced renal cell carcinoma. Ann. Oncol. 31(8), 1030–1039 (2020).
PubMed
Google Scholar
30.
Choueiri TK, Powles T, Burotto M et al. Nivolumab plus cabozantinib versus sunitinib for advanced renal-cell carcinoma. N. Engl. J. Med. 384(9), 829–841 (2021).
PubMed
Google Scholar
31.
Hoaglin DC, Hawkins N, Jansen JP et al. Conducting indirect-treatment-comparison and network-meta-analysis studies: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 2. Value Health 14(4), 429–437 (2011).
PubMed
Google Scholar
32.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2020).
Google Scholar
33.
Stan Development Team. RStan: the R interface to Stan (2021). http://mc-stan.org/users/interfaces/rstan
Google Scholar
34.
Brooks SP, Gelman A. General methods for monitoring convergence of iterative simulations. J. Comput. Graphical Stat. 7(4), 434–455 (1998).
Google Scholar
35.
Roy V. Convergence diagnostics for Markov chain Monte Carlo. Ann. Rev. Stat. App. 7, 387–412 (2020).
Google Scholar
36.
Motzer RJ, Robbins PB, Powles T et al. Avelumab plus axitinib versus sunitinib in advanced renal cell carcinoma: biomarker analysis of the phase 3 JAVELIN Renal 101 trial. Nat. Med. 26(11), 1733–1741 (2020).
PubMed
Google Scholar
37.
Gerlinger M, Rowan AJ, Horswell S et al. Intratumor Heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366(10), 883–892 (2012).
PubMed
Google Scholar
38.
Beksac AT, Paulucci DJ, Blum KA, Yadav SS, Sfakianos JP, Badani KK. Heterogeneity in renal cell carcinoma. Urol. Oncol. 35(8), 507–515 (2017).
PubMed
Google Scholar
39.
Mcgregor BA, Geynisman DM, Burotto M et al. Efficacy outcomes of nivolumab + cabozantinib versus pembrolizumab + axitinib in patients with advanced renal cell carcinoma (aRCC): matching-adjusted indirect comparison (MAIC). J. Clin. Oncol. 39(Suppl. 15), 4578–4578 (2021).
Google Scholar
40.
Phillippo DM, Dias S, Ades AE et al. Multilevel network meta-regression for population-adjusted treatment comparisons. J. R. Stat. Soc. Ser. A Stat. Soc. 183(3), 1189–1210 (2020).
PubMed
Google Scholar
41.
Guadalupi V, Carteni G, Iacovelli R et al. Second-line treatment in renal cell carcinoma: clinical experience and decision making. Ther. Adv. Urol. 13, (2021).
Crossref
PubMed
Google Scholar
42.
Ouwens Mario. Novel approaches to common challenges in modelling of survival outcomes for cost-effectiveness analyses in oncology. W8 Advanced Workshop, ISPOR 2018. Barcelona, Spain (2018). www.ispor.org/docs/default-source/presentations/91714pdf.pdf?sfvrsn=a5b2756f_0
Google Scholar
43.
Wiecek W, Karcher H. Nivolumab versus cabozantinib: comparing overall survival in metastatic renal cell carcinoma. PLoS One 11(6), e0155389 (2016).
PubMed
Google Scholar
44.
Toor K, Middleton MR, Chan K, Amadi A, Moshyk A, Kotapati S. Comparative efficacy and safety of adjuvant nivolumab versus other treatments in adults with resected melanoma: a systematic literature review and network meta-analysis. BMC Cancer 21(1), 3 (2021).
PubMed
Google Scholar
45.
Cope S, Chan K, Campbell H et al. A Comparison of alternative network meta-analysis methods in the presence of nonproportional hazards: a case study in first-line advanced or metastatic renal cell carcinoma. Value Health 26(4), 465–476 (2023).
PubMed
Google Scholar
|