  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 modelling 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 non-proportionality 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.
