A multistate platform model for time‐to‐event endpoints in oncology clinical trials

Abstract

Abstract A multistate platform model was developed to describe time‐to‐event (TTE) endpoints in an oncology trial through the following states: initial, tumor response (TR), progressive disease (PD), overall survival (OS) event (death), censor to the last evaluable tumor assessment (progression‐free survival [PFS] censor), and censor to study end (OS censor), using an ordinary differential equation framework. Two types of piecewise functions were used to describe the hazards for different events. Piecewise surge functions were used for events that require tumor assessments at the scheduled study visit times (TR, PD, and PFS censor). Piecewise constant functions were used to describe hazards for events that occur evenly throughout the study (OS event and OS censor). The multistate TTE model was applied to describe TTE endpoints from a published phase III study. The piecewise surge functions well‐described the observed surges of hazards/events for TR, PD, PFS, and OS occurring near scheduled tumor assessments and showed good agreement with all Kaplan‐Meier curves. With the flexibility of piecewise hazard functions, the model was able to evaluate covariate effects in a time‐variant fashion to better understand the temporal patterns of disease prognosis through different disease states. This model can be applied to advance the field of oncology trial design and optimization by: (1) enabling robust estimations of baseline hazards and covariate effects for multiple TTE endpoints, (2) providing a platform model for understanding the composition and correlations between different TTE endpoints, and (3) facilitating oncology trial design optimization through clinical trial simulations.