Speaker
Description
Accurate analysis of multiple time-to-event endpoints is a persistent challenge in clinical research, where patients may experience several recurrent non-fatal events alongside a competing fatal event. Conventional survival analysis approaches, such as time-to-first-event analyses or the Cox proportional hazards model, often neglect recurrent events or assume independence between event types, which can lead to biased estimates and a loss of clinical information. Although several methods have been proposed to address these issues, including extensions of Cox models, frailty-based models, multistate approaches, and composite endpoint frameworks, their relative performance in realistic data settings remains insufficiently understood.
In this simulation study, we aim to systematically evaluate and compare statistical methods for analyzing multiple time-to-event endpoints in the presence of recurrent and competing events. We focus on Cox-based approaches (e.g., Andersen–Gill and Prentice–Williams–Peterson models), methods that account for ordered endpoints (e.g., Win Ratio and Win Odds), and weight-based composite endpoint methods (e.g., the Wei–Lachin approach and weighted all-cause hazard ratio). We generate synthetic individual patient data under a range of clinically motivated scenarios that vary in event rates, degree of dependence between events, treatment effects, and censoring levels. Event times are simulated using flexible models that allow control over recurrence intensity and dependence between non-fatal and fatal events.
Each method will be assessed according to its ability to recover true treatment effects under different conditions. Key performance metrics will include bias, empirical power, coverage probability, and mean squared error. We will also evaluate robustness under deviations from key assumptions, such as proportional hazards or event independence. The simulation design follows established recommendations for transparent and reproducible simulation studies (Burton et al., 2006; Morris et al., 2019).
Preliminary findings suggest that commonly used Cox-based approaches perform well in simple scenarios but tend to underestimate treatment effects when strong dependencies exist between recurrent and fatal events. More flexible modeling strategies that explicitly represent such dependencies are expected to yield more accurate and interpretable estimates.
This study will provide a comprehensive and transparent evaluation of current methods for analyzing multiple time-to-event endpoints and identify areas where methodological development is needed. The results will support researchers in choosing appropriate analytical approaches and contribute to improving the planning and interpretation of clinical studies that involve recurrent and competing events.
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