Multilevel Network Meta-Regression With a Survival Outcome and an Application to Non-Small Cell Lung Cancer

In biopharmaceutical studies, it is often of interest to compare the effects of two treatments (say, A and B), but data that contain a direct comparison are not available. However, there may be studies that compare them against another competitor (say, C). In this case, an indirect comparison of A versus B through C needs to be conducted. In this article, we consider the scenario where individual participant data (IPD) is available for the comparison of A versus C, but only aggregate-level data (AgD), for example, from a publication, is available for the comparison of B versus C. For such analysis, multilevel network meta-regression (ML-NMR) is advantageous since it can combine evidence from multiple trials with either IPD or AgD and can compare the treatments of interest in any target population. Most of the existing ML-NMR studies have focused on binary and continuous outcomes, while, relatively, research on censored survival outcomes remains limited with perhaps only one study modeling the marginal likelihood for AgD. Here, we aim to extend ML-NMR for time-to-event outcomes. We consider multiple popular parametric survival models and develop Bayesian estimation approaches built on mean and median survival. Extensive simulations show satisfactory performance. We further consider a case study on early-stage non-small cell lung cancer (NSCLC) overall survival. Emulation analyses of the Surveillance, Epidemiology, and End Results (SEER)-Medicare data are conducted. It is found that limited resection (LR) with adjuvant chemotherapy (ACT) prolongs survival compared to LR or lobectomy without ACT.