The Scale Transformed Power Prior for Time-To-Event Data

Abstract

In survival analysis, borrowing information from historical data can increase precision and power. However existing methods often assume that both current and historical data are of the same type. This assumption becomes problematic when data types differ, such as in cancer trials where phase 2 studies may use binary endpoints (e.g., response rates) while phase 3 studies typically use timeto-event endpoints. To address this limitation, we propose the partial-borrowing scale-transformed power prior (straPP), specifically designed for survival models with heterogeneous historical data. By using a functional rescaling based on the Fisher information matrices, the straPP aligns parameter vectors across differing data types, enabling partial borrowing of historical information while mitigating biases associated with borrowing from mismatched endpoints. Additionally, we introduce the generalized scale-transformed power prior (Gen-straPP) to further guard against biases in circumstances where scaling alone is insufficient. Through simulations and analyses of real cancer trial data from the Eastern Cooperative Oncology Group, we demonstrate that the (Gen-) straPP can outperform traditional priors in controlling type I error, power, coverage probabilities, and model fit, making it a robust choice for time-to-event analysis in these contexts.