32: Estimating the Prognostic Effect in Biomarker Real-World Studies

Music City Center 

The selection of patient populations based on biomarkers is crucial for enhancing the precision and efficacy of targeted cancer therapies. An integrated evidence generation plan for targeted therapies should address critical questions related to biomarkers, including understanding prognostic effects that indicate the likelihood of overall survival.
Assessing these effects in real-world retrospective studies poses numerous challenges, such as confounding factors, missing data, and biases like immortal time bias. Although multivariate Cox proportional hazards (PH) models are widely used, they may not fully address all potential issues arising from real-world data.
In this talk, we will present a comparison of different methods for estimating the prognostic effects of biomarkers, using simulated data that mimics real-life scenarios. These methods include multivariate Cox PH models, a machine learning approach using random survival forests, and a propensity score-based method inspired by causal inference. We will also demonstrate cases with and without missing data imputation, as well as approaches for handling the immortal time bias.

Prognostic effect

random survival forest

immortal time bias

real-world evidence 

Biopharmaceutical Section