Multistate model for OS prediction: a case study in oncology clinical trial

Music City Center 

Upon completion of a phase II clinical trial, critical decision needs to be made regarding the viability of advancing to phase III development. In early phase oncology trials for NSCLC, the overall response rate (ORR) is commonly employed to assess the likelihood of success. However, the relationship between ORR and the long-term endpoint overall survival (OS) remains uncertain, rendering ORR an inadequate surrogate for OS. Moreover, OS data collected during phase II trials typically lack maturity due to limited follow-up time. In this project, we explore the use of multistate models for prediction of long-term OS based on the available phase II tumor shrinkage/response data and information borrowed from historical/external control arm. The multistate models account for different patient states. We illustrate the method via data from JDQ443 KontRASt-01 trial and historical control-docetaxel arm of CANOPY-2 trial for non-small cell lung cancer (NSCLC) and show how the median OS and the hazard ratio can be effectively predicted and ultimately prove how instrumental is the method in informing the go/no-go decision for future phase III trials. In the meantime, analysis based on simulation also shows favorable operating characteristics of the method and suggests that the method would lead to efficient early go/no-go decision in the presence of limited follow-up and small sample size.

Overall Survival Prediction

multistate model

Inverse Probability of Treatment Weighting (IPTW)