×

Bayesian multivariate network meta-analysis model for the difference in restricted mean survival times. (English) Zbl 07929498

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
Full Text: DOI

References:

[1] LumleyT. Network meta‐analysis for indirect treatment comparisons. Stat Med. 2002;21(16):2313‐2324.
[2] LuG, AdesAE. Combination of direct and indirect evidence in mixed treatment comparisons. Stat Med. 2004;23(20):3105‐3124.
[3] LuG, AdesAE. Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc. 2006;101(474):12‐459. · Zbl 1119.62354
[4] CrequitP, TrinquartL, YavchitzA, et al. Wasted research when systematic reviews fail to provide a complete and up‐to‐date evidence synthesis: the example of lung cancer. BMC Med. 2016;14:8.
[5] CrequitP, ChaimaniA, YavchitzA, et al. Comparative efficacy and safety of second‐line treatments for advanced non‐small cell lung cancer with wild‐type or unknown status for epidermal growth factor receptor: a systematic review and network meta‐analysis. BMC Med. 2017;15(1):193.
[6] NikolakopoulouA, MavridisD, FurukawaTA, et al. Living network meta‐analysis compared with pairwise meta‐analysis in comparative effectiveness research: empirical study. BMJ. 2018;360:k585.
[7] VickersAD, WinfreeKB, CuyunCG, 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.
[8] DiasS, SuttonAJ, AdesAE, WeltonNJ. 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.
[9] TrinquartL, JacotJ, ConnerSC, PorcherR. Comparison of treatment effects measured by the Hazard ratio and by the ratio of restricted mean survival times in oncology randomized controlled trials. J Clin Oncol. 2016;34(15):1813‐1819.
[10] RahmanR, FellG, VentzS, et al. Deviation from the proportional hazards assumption in randomized phase 3 clinical trials in oncology: prevalence, associated factors, and implications. Clin Cancer Res. 2019;25(21):6339‐6345.
[11] OuwensMJ, PhilipsZ, JansenJP. Network meta‐analysis of parametric survival curves. Res Synth Methods. 2010;1(3-4):258‐271.
[12] CopeS, ChanK, JansenJP. Multivariate network meta‐analysis of survival function parameters. Res Synth Methods. 2020;11(3):443‐456.
[13] JansenJP. Network meta‐analysis of survival data with fractional polynomials. BMC Med Res Methodol. 2011;11:61.
[14] WikstenA, HawkinsN, PiephoHP, GsteigerS. Nonproportional hazards in network meta‐analysis: efficient strategies for model building and analysis. Value Health. 2020;23(7):918‐927.
[15] FreemanSC, CarpenterJR. Bayesian one‐step IPD network meta‐analysis of time‐to‐event data using Royston‐Parmar models. Res Synth Methods. 2017;8(4):451‐464.
[16] PakK, UnoH, KimDH, et al. Interpretability of cancer clinical trial results using restricted mean survival time as an alternative to the Hazard ratio. JAMA Oncol. 2017;3(12):1692‐1696.
[17] WeirIR, MarshallGD, SchneiderJI, et al. Interpretation of time‐to‐event outcomes in randomized trials: an online randomized experiment. Ann Oncol. 2019;30(1):96‐102.
[18] PetitC, BlanchardP, PignonJP, LuezaB. 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.
[19] WeirIR, TianL, TrinquartL. Multivariate meta‐analysis model for the difference in restricted mean survival times. Biostatistics. 2019;22:82‐96.
[20] AchanaFA, CooperNJ, BujkiewiczS, et al. Network meta‐analysis of multiple outcome measures accounting for borrowing of information across outcomes. BMC Med Res Methodol. 2014;14:92.
[21] American cancer socity‐lung cancer; 2019. https://www.cancer.org/cancer/lung‐cancer.html.
[22] AuliacJB, ChouaidC, GreillierL, et al. Randomized open‐label non‐comparative multicenter phase II trial of sequential erlotinib and docetaxel versus docetaxel alone in patients with non‐small‐cell lung cancer after failure of first‐line chemotherapy: GFPC 10.02 study. Lung Cancer. 2014;85(3):415‐419.
[23] HanJY, LeeSH, YooNJ, et al. A randomized phase II study of gefitinib plus simvastatin versus gefitinib alone in previously treated patients with advanced non‐small cell lung cancer. Clin Cancer Res. 2011;17(6):1553‐1560.
[24] KimY, ChoE, SymS, et al. Randomized Phase II Study of Pemetrexed Versus Gefitinib in Previously Treated Patients with Advanced Non‐small Cell Lung Cancer. Chicaogo, IL: American Society of Clinical Oncology; 2014.
[25] DiasS, AdesA, WeltonNJ, et al. Network Meta‐Analysis for Decision‐Making. Hoboken, NJ: Wiley; 2018.
[26] RileyRD, PriceMJ, JacksonD, et al. Multivariate meta‐analysis using individual participant data. Res Synth Methods. 2015;6(2):157‐174.
[27] BurkeDL, BujkiewiczS, RileyRD. Bayesian bivariate meta‐analysis of correlated effects: impact of the prior distributions on the between‐study correlation, borrowing of strength, and joint inferences. Stat Methods Med Res. 2018;27(2):428‐450.
[28] CaldwellDM, AdesAE, HigginsJP. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. Br Med J. 2005;331(7521):897‐900.
[29] PepeMS, FlemingTR. Weighted Kaplan‐Meier statistics: a class of distance tests for censored survival data. Biometrics. 1989;45:497‐507. · Zbl 0715.62087
[30] PepeMS, FlemingEJ. Weighted Kaplan‐Meier statistics: large sample and optimality considerations. J Royal Stat Soc Ser B(Methodol). 1991;53:341‐352. · Zbl 0800.62218
[31] TianL, FuH, RubergSJ, UnoH, WeiLJ. Efficiency of two sample tests via the restricted mean survival time for analyzing event time observations. Biometrics. 2018;74(2):694‐702. · Zbl 1414.62479
[32] JacksonD, BujkiewiczS, LawM, RileyRD, WhiteIR. A matrix‐based method of moments for fitting multivariate network meta‐analysis models with multiple outcomes and random inconsistency effects. Biometrics. 2018;74(2):548‐556. · Zbl 1415.62108
[33] LuG, AdesA. Modeling between‐trial variance structure in mixed treatment comparisons. Biostatistics. 2009;10(4):792‐805. · Zbl 1437.62544
[34] WeiY, HigginsJP. Bayesian multivariate meta‐analysis with multiple outcomes. Stat Med. 2013;32(17):2911‐2934.
[35] BarnardJ, McCullochR, MengX. Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Stat Sin. 2000;10(4):1281‐1311. · Zbl 0980.62045
[36] BrooksSP, GelmanA. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat. 1998;7(4):434‐455.
[37] CasellaG, BergerRL. Statistical Inference. 2nd ed.Belmon, CA: Duxbury; 2002.
[38] TrinquartL, IoannidisJP, ChatellierG, RavaudP. A test for reporting bias in trial networks: simulation and case studies. BMC Med Res Methodol. 2014;14:112.
[39] PestineE, StokesA, TrinquartL. Representation of obese participants in obesity‐related cancer randomized trials. Ann Oncol. 2018;29(7):1582‐1587.
[40] BarianiGM, deCelis FerrariAC, PrecivaleM, et al. Sample size calculation in oncology trials: quality of reporting and implications for clinical cancer research. Am J Clin Oncol. 2015;38(6):570‐574.
[41] LuezaB, RotoloF, BonastreJ, PignonJP, MichielsS. Bias and precision of methods for estimating the difference in restricted mean survival time from an individual patient data meta‐analysis. BMC Med Res Methodol. 2016;16:37.
[42] BenderR, AugustinT, BlettnerM. Generating survival times to simulate cox proportional hazards models. Stat Med. 2005;24(11):1713‐1723.
[43] ShepherdFA, DanceyJ, RamlauR, 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.
[44] BorghaeiH, Paz‐AresL, HornL, et al. Nivolumab versus Docetaxel in advanced nonsquamous non‐small‐cell lung cancer. N Engl J Med. 2015;373(17):1627‐1639.
[45] UhlmannL, JensenK, KieserM. Hypothesis testing in Bayesian network meta‐analysis. BMC Med Res Methodol. 2018;18(1):128.
[46] SeideSE, RoverC, FriedeT. Likelihood‐based random‐effects meta‐analysis with few studies: empirical and simulation studies. BMC Med Res Methodol. 2019;19(1):16.
[47] ThorlundK, ThabaneL, MillsEJ. Modelling heterogeneity variances in multiple treatment comparison meta‐analysis—are informative priors the better solution?BMC Med Res Methodol. 2013;13:2.
[48] DoneganS, WilliamsonT, D’AkessandroU, et al. Assessing key assumptions of network meta‐analysis: a review of methods. Res Synth Methods. 2013;4(4):291‐323.
[49] DiasS, WeltonNJ, SuttonAJ, CaldwellDM, LuG, AdesAE. Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials. Med Decis Making. 2013;33(5):641‐656.
[50] RileyRD, ThompsonJR, AbramsKR. An alternative model for bivariate random‐effects meta‐analysis when the within‐study correlations are unknown. Biostatistics. 2008;9(1):172‐186. · Zbl 1274.62861
[51] EfthimiouO, MavridisD, CiprianiA, LeuchtS, BagosP, SalantiG. An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios. Stat Med. 2014;33(13):2275‐2287.
[52] HongH, FuH, PriceKL, CarlinBP. Incorporation of individual‐patient data in network meta‐analysis for multiple continuous endpoints, with application to diabetes treatment. Stat Med. 2015;34(20):2794‐2819.
[53] HongH, ChuH, ZhangJ, CarlinBP. A Bayesian missing data framework for generalized multiple outcome mixed treatment comparisons. Res Synth Methods. 2016;7(1):6‐22.
[54] VeronikiAA, VasiliadisHS, HigginsJP, SalantiG. Evaluation of inconsistency in networks of interventions. Int J Epidemiol. 2013;42(1):332‐345.
[55] Gajic‐VeljanoskiO, CheungAM, BayoumiAM, TomlinsonG. The choice of a noninformative prior on between‐study variance strongly affects predictions of future treatment effect. Med Decis Making. 2013;33(3):356‐368.
[56] LambertPC, SuttonAJ, BurtonPR, AbramsKR, JonesDR. How vague is vague? a simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Stat Med. 2005;24(15):2401‐2428.
[57] IshakKJ, PlattRW, JosephL, HanleyJA. Impact of approximating or ignoring within‐study covariances in multivariate meta‐analyses. Stat Med. 2008;27(5):670‐686.
[58] HuD, WangC, O’ConnorAM. A likelihood ratio test for the homogeneity of between‐study variance in network meta‐analysis. 2021. https://doi.org/10.21203/rs.3.rs‐224184/v1 · doi:10.21203/rs.3.rs‐224184/v1
[59] RileyRD, AbramsKR, SuttonAJ, LambertPC, ThompsonJR. Bivariate random‐effects meta‐analysis and the estimation of between‐study correlation. BMC Med Res Methodol. 2007;7:3.
[60] MaddenLV, PiephoHP, PaulPA. Statistical models and methods for network meta‐analysis. Phytopathology. 2016;106(8):792‐806.
[61] TurnerRM, Dominguez‐IslasCP, JacksonD, et al. Incorporating external evidence on between‐trial heterogeneity in network meta‐analysis. Stat Med. 2019;38(8):1321‐1335.
[62] vanHouwelingenHC, ArendsLR, StijnenT. Advanced methods in meta‐analysis: multivariate approach and meta‐regression. Stat Med. 2002;21(4):589‐624.
[63] BujkiewiczS, ThompsonJR, SuttonAJ, et al. Multivariate meta‐analysis of mixed outcomes: a Bayesian approach. Stat Med. 2013;32(22):3926‐3943.
[64] GuyotP, AdesAE, OuwensMJ, WeltonNJ. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan‐Meier survival curves. BMC Med Res Methodol. 2012;12:9.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.