A Nonmechanistic Parametric Modeling Approach for Benchmark Dose Estimation of Event-Time Data.

We propose benchmark dose estimation for event-time data, using a two-step approach. This approach avoids estimation of complex models and has been previously shown to give robust results for summarizing relevant parameters for risk assessment. In the first step, the probability of the event of interest to occur (in a certain time interval) is described as a function of time, resulting in an event-time model; such a model is fitted allowing an individual curve for each dose, and relevant estimates are extracted. In the second step, a dose-response model is fitted to the estimates of t obtained from the event-time model in the first step. Given a predefined benchmark response, the benchmark dose is then estimated from the resulting model. This novel approach is demonstrated in two examples. Our application of the time-to-event model showed a gain in power compared to the traditional analysis of end-of-study summary data.