Time-to-event analysis often relies on prior parametric assumptions, or, if a non-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. In this paper, 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 the risk of first hospitalization for heart failure.

翻译：时间事件分析通常依赖于先验的参数假设，或者若选择非参数方法则依赖于Cox模型。这本质上与比例风险假设相关联，若该假设不成立，分析结果可能无效。此外，多数解释聚焦于风险比，而这常被误解为相对风险。本文提出一种替代当前方法论的方法，用于评估两组情境下的治疗效应，该方法不依赖比例风险假设，而是基于比例风险假设。具体而言，我们提出一种新的非参数模型，在风险比随时间恒定的假设下，直接估计两组经历事件的相对风险。除这一相对度量外，该模型还可计算绝对度量所需治疗人数，从而提供对数据简单而全面的解读可能。我们通过模拟研究验证了方法的有效性，并将其应用于一项大规模随机对照试验的数据，该试验研究了达格列净对首次因心力衰竭住院风险的影响。