The time-dependent "cure-death" model investigating two equally important endpoints simultaneously in trials treating high-risk patients with resistant pathogens.

A variety of primary endpoints are used in clinical trials treating patients with severe infectious diseases, and existing guidelines do not provide a consistent recommendation. We propose to study simultaneously two primary endpoints, cure and death, in a comprehensive multistate cure-death model as starting point for a treatment comparison. This technique enables us to study the temporal dynamic of the patient-relevant probability to be cured and alive. We describe and compare traditional and innovative methods suitable for a treatment comparison based on this model. Traditional analyses using risk differences focus on one prespecified timepoint only. A restricted logrank-based test of treatment effect is sensitive to ordered categories of responses and integrates information on duration of response. The pseudo-value regression provides a direct regression model for examination of treatment effect via difference in transition probabilities. Applied to a topical real data example and simulation scenarios, we demonstrate advantages and limitations and provide an insight into how these methods can handle different kinds of treatment imbalances. The cure-death model provides a suitable framework to gain a better understanding of how a new treatment influences the time-dynamic cure and death process. This might help the future planning of randomised clinical trials, sample size calculations, and data analyses.