Dealing with censoring in a network meta-analysis of time-to-event data

Background The Health Technology Assessment agencies typically require an economic evaluation considering a lifetime horizon for interventions affecting survival. However, survival data are often censored and are typically analyzed assuming the censoring mechanism independent of the event process. This assumption may lead to biased results when the censoring mechanism is informative. Methods We propose a flexible approach to jointly model the participants experiencing an event and censored participants by incorporating the pattern-mixture (PM) model in the fractional polynomial (FP) model within the network meta-analysis (NMA) framework. We introduce the informative censoring hazard ratio parameter that quantifies the departure from the censored at random assumption. The FP-PM model is exemplified in an NMA of the overall survival from non-small cell lung carcinoma studies using Bayesian methods. Results The results on hazard ratio and survival from the FP-PM model are similar to those from the FP model. However, the posterior standard deviation of the hazard ratio is slightly greater when censored data are modeled because the uncertainty induced by censoring is naturally accounted for in the FP-PM model. The between-study standard deviation is almost identical in both models due to the low censoring rate across the studies. At the end of the corresponding studies, the informative censoring hazard ratio demonstrated a possible departure from the censored at random assumption for gefitinib and best supportive care. Conclusions The proposed method offers a comprehensive sensitivity analysis framework to examine the robustness of the NMA results to clinically plausible censoring scenarios.