A Bayesian Approach to Compare Accumulating Survival Data From Clinical Practice With RCT Data: A Case Study in...

Survival outcomes observed in randomized controlled trials (RCTs) may not always be generalizable to clinical practice. Evaluating whether treatment outcomes in clinical practice are similar to those in RCTs shortly after a new medicine is introduced is important for making informed decisions. Therefore, we aimed to develop a Bayesian model that compares survival data from clinical practice that accumulates over time with static survival data from RCTs, thereby providing rapid and easily interpretable results that can inform clinical and policy-related decision-making. We developed a Bayesian survival model that sequentially updates estimates as new data become available. We designed the model to incorporate static RCT data with accumulating clinical practice data. We used sequential hypothesis testing with Bayes factors to assess the strength of the evidence for different hazard ratio (HR) thresholds (i.e., ranging from HR > 1.0 to > 2.0 and HR < 0.5 to < 1.0). We applied the model to two datasets comprising survival data from clinical practice and an RCT for lung cancer patients treated with pembrolizumab plus chemotherapy (dataset 1) and pembrolizumab monotherapy (dataset 2). For dataset 1, the posterior model checks showed a misfit between the model and the data after 15 months, potentially due to channeling bias. The model fit should be improved before reliable estimates can be obtained. For dataset 2, the model estimated precise HRs 10 months before the end of data accumulation. Sequential hypothesis testing with Bayes factors provided easily interpretable results, with very strong evidence for an HR > 1.0 and strong evidence for an HR > 1.2. In conclusion, provided the posterior check shows an acceptable model fit, our Bayesian survival model with sequential hypothesis testing using Bayes factors can provide rapid and easily interpretable results for decision-making.