Efficacy analysis and prediction in phase II clinical trials of non-small cell Lung Cancer

Abstract

The American Cancer Society estimates that lung cancer is the most common type of cancer and nearly 25% cancer deaths result from lung cancer, of which three subtypes account for 80% of cases. This is the rationale behind our study's selection of NSCLC subtypes. Moreover, drugs approved after passing clinical trials. Phase II clinical studies rely on "interim" data about safety and efficacy, enabling quicker drug approval. However, patient’s diversity is a cause of treatment failure, as the risk of adverse events or treatment failure can be influenced by individual genetic variation. To solve this, finding an efficacy endpoint (PFS, ORR, OS) type from clinical studies allow the effectiveness of treatment to be quantified. In our study, we gathered effective endpoints and analysed to determine whether any relationships existed and if so, constructed a predictive model with the best predictor endpoint