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Network meta-analysis (NMA) allows the combination of direct and indirectevidence from a set of randomized clinical trials. Performing NMA usingindividual patient data (IPD) is considered as a "gold standard" approach as itprovides several advantages over NMA based on aggregate data. For example, itallows to perform advanced modelling of covariates or covariate-treatmentinteractions. An important issue in IPD NMA is the selection of influentialparameters among terms that account for inconsistency, covariates,covariate-by-treatment interactions or non-proportionality of treatments effectfor time to event data. This issue has not been deeply studied in theliterature yet and in particular not for time-to-event data. A major difficultyis to jointly account for between-trial heterogeneity which could have a majorinfluence on the selection process. The use of penalized generalized mixedeffect model is a solution, but existing implementations have severalshortcomings and an important computational cost that precludes their use forcomplex IPD NMA. In this article, we propose a penalized Poisson regressionmodel to perform IPD NMA of time-to-event data. It is based only on fixedeffect parameters which improve its computational cost over the use of randomeffects. It could be easily implemented using existing penalized regressionpackage. Computer code is shared for implementation. The methods were appliedon simulated data to illustrate the importance to take into account betweentrial heterogeneity during the selection procedure. Finally, it was applied toan IPD NMA of overall survival of chemotherapy and radiotherapy innasopharyngeal carcinoma.

Penalized Poisson model for network meta-analysis of individual patient time-to-event data