A non-parametric proportional risk model to assess a treatment effect in time-to-event data

Time-to-event analysis often relies on prior parametric assumptions, or, if a non- or semi-parametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk, the ratio of the cumulative distribution functions. 
    In the corresponding manuscript, we introduce an alternative to current methodology for assessing a treatment effect in a two-group situation, not relying on the proportional hazards assumption but assuming proportional risks. Precisely, we propose a new non-parametric model to directly estimate the relative risk of two groups to experience an event under the assumption that the risk ratio is constant over time. In addition to this relative measure, our model allows for calculating the number needed to treat as an absolute measure, providing the possibility of an easy and holistic interpretation of the data. 
    We demonstrate the validity of the approach by means of a simulation study and present an application to data from a large randomized controlled trial  investigating the effect of dapagliflozin on all-cause mortality.

This upload contains the code, (simulated) data sets and (intermediate) results to reproduce the results and plots presented in the manuscript. For details we refer to the included Read Me file.