Penalized Poisson model for network meta-analysis of individual patient time-to-event data.
frequentist
individual patient data
lasso
model selection
network meta-analysis
penalized likelihood
time-to-event data
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 01 2022
30 01 2022
Historique:
revised:
14
10
2021
received:
08
12
2020
accepted:
15
10
2021
pubmed:
29
10
2021
medline:
1
4
2022
entrez:
28
10
2021
Statut:
ppublish
Résumé
Network meta-analysis (NMA) allows the combination of direct and indirect evidence from a set of randomized clinical trials. Performing NMA using individual patient data (IPD) is considered as a "gold standard" approach as it provides several advantages over NMA based on aggregate data. For example, it allows to perform advanced modeling of covariates or covariate-treatment interactions. An important issue in IPD NMA is the selection of influential parameters among terms that account for inconsistency, covariates, covariate-by-treatment interactions or nonproportionality of treatments effect for time to event data. This issue has not been deeply studied in the literature yet and in particular not for time-to-event data. A major difficulty is to jointly account for between-trial heterogeneity which could have a major influence on the selection process. The use of penalized generalized mixed effect model is a solution, but existing implementations have several shortcomings and an important computational cost that precludes their use for complex IPD NMA. In this article, we propose a penalized Poisson regression model to perform IPD NMA of time-to-event data. It is based only on fixed effect parameters which improve its computational cost over the use of random effects. It could be easily implemented using existing penalized regression package. Computer code is shared for implementation. The methods were applied on simulated data to illustrate the importance to take into account between trial heterogeneity during the selection procedure. Finally, it was applied to an IPD NMA of overall survival of chemotherapy and radiotherapy in nasopharyngeal carcinoma.
Types de publication
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
340-355Informations de copyright
© 2021 John Wiley & Sons Ltd.
Références
Berlin JA, Santanna J, Schmid CH, Szczech LA, Feldman HI. Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head. Stat Med. 2002;21:371-387.
Lambert PC, Sutton AJ, Abrams KR, Jones DR. A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis. J Clin Epidemiol. 2002;55:86-94.
Debray TP, Schuit E, Efthimiou O, et al. An overview of methods for network meta-analysis using individual participant data: when do benefits arise? Stat Methods Med Res. 2018;27(5):1351-1364.
Debray TP, Moons KG, Abo-Zaid GM, Koffijberg H, Riley RD. Individual participant data meta-analysis for a binary outcome: one-stage or two-stage? PLoS One. 2013;8(4):e60650.
Freeman SC, Carpenter JR. Bayesian one-step IPD network meta-analysis of time-to-event data using Royston-Parmar models. Res Synth Methods. 2017;8(4):451-464.
Ribassin-Majed L, Marguet S, Lee AWM, et al. What is the best treatment of locally advanced nasopharyngeal carcinoma? an individual patient data network meta-analysis. J Clin Oncol. 2017;35(5):498-505.
Crowther MJ, Riley RD, Staessen JA, Wang J, Gueyffier F, Lambert PC. Individual patient data meta-analysis of survival data using Poisson regression models. BMC Med Res Methodol. 2012;12(1):34.
Lu G, Ades AE. Assessing evidence inconsistency in mixed treatment comparisons. J Am Stat Assoc. 2006;101(474):447-459.
Freeman SC, Fisher D, Tierney JF, Carpenter JR. A framework for identifying treatment-covariate interactions in individual participant data network meta-analysis. Res Synth Methods. 2018;9(3):393-407.
Seo M, White IR, Furukawa TA, et al. Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis. Stat Med. 2021 Mar 15;40(6):1553-1573.
Groll A, Tutz G. Variable selection for generalized linear mixed models by L 1-penalized estimation. Stat Comput. 2014;24(2):137-154.
Schelldorfer J, Meier L, Bühlmann P. Glmmlasso: an algorithm for high-dimensional generalized linear mixed models using ℓ1-penalization. J Comput Graph Stat. 2014;23(2):460-477.
Fort G, Ollier E, Samson A. Stochastic proximal-gradient algorithms for penalized mixed models. Stat Comput. 2019;29(2):231-253.
Ollier E, Viallon V. Regression modelling on stratified data with the lasso. Biometrika. 2017;104(1):83-96.
Perperoglou A. Cox models with dynamic ridge penalties on time-varying effects of the covariates. Stat Med. 2014;33(1):170-180.
Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Stat Methodol. 1996;58(1):267-288.
Zou H. The adaptive lasso and its oracle properties. J Am Stat Assoc. 2006;101(476):1418-1429.
Goeman JJ. L1 penalized estimation in the cox proportional hazards model. Biom J. 2010;52(1):70-84.
Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression. Ann Stat. 2004;32(2):407-499.
Moriña D, Navarro A. The R package survsim for the simulation of simple and complex survival data. J Stat Softw. 2014;59(2):1-20.
Lee JD, Sun DL, Sun Y, Taylor JE. Exact post-selection inference, with application to the lasso. Ann Stat. 2016;44(3):907-927.
Taylor J, Tibshirani RJ. Statistical learning and selective inference. Proc Natl Acad Sci. 2015;112(25):7629-7634.
Baghishani H, Mohammadzadeh M. Asymptotic normality of posterior distributions for generalized linear mixed models. J Multivar Anal. 2012;111:66-77.
Davies AL, Galla T. Degree irregularity and rank probability bias in network meta-analysis. Res Synth Methods. 2021;12(3):316-332.
Blanchard P, Lee A, Marguet S, et al. Chemotherapy and radiotherapy in nasopharyngeal carcinoma: an update of the MAC-NPC meta-analysis. Lancet Oncol. 2015;16(6):645-655.
Murray TA, Hobbs BP, Lystig TC, Carlin BP. Semiparametric Bayesian commensurate survival model for post-market medical device surveillance with non-exchangeable historical data. Biometrics. 2014;70(1):185-191.
Ollier E, Samson A, Delavenne X, Viallon V. A SAEM algorithm for fused lasso penalized non-linear mixed effect models: application to group comparison in pharmacokinetics. Comput Stat Data Anal. 2016;95:207-221.
Viallon V, Lambert-Lacroix S, Höfling H, Picard F. On the robustness of the generalized fused lasso to prior specifications. Stat Comput. 2016;26(1):285-301.
Hua H, Burke DL, Crowther MJ, Ensor J, Tudur Smith C, Riley RD. One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information. Stat Med. 2017;36(5):772-789.