Count endpoints are common in clinical trials, particularly for recurrent events such as hypoglycemia. When interest centers on comparing overall event rates between treatment groups, negative binomial (NB) regression is widely used because it accommodates overdispersion and requires only event counts and exposure times. However, NB regression can be numerically unstable when events are sparse, and the efficiency gains from baseline covariate adjustment may be sensitive to model misspecification. We propose an empirical method that targets the same marginal estimand as NB regression -- the ratio of marginal event rates -- while avoiding distributional assumptions on the count outcome. Simulation studies show that the empirical method maintains appropriate Type I error control across diverse scenarios, including extreme overdispersion and zero inflation, achieves power comparable to NB regression, and yields consistent efficiency gains from baseline covariate adjustment. We illustrate the approach using severe hypoglycemia data from the QWINT-5 trial comparing insulin efsitora alfa with insulin degludec in adults with type 1 diabetes. In this sparse-event setting, the empirical method produced stable marginal rate estimates and rate ratios closely aligned with observed rates, while NB regression exhibited greater sensitivity and larger deviations from the observed rates in the sparsest intervals. The proposed empirical method provides a robust and numerically stable alternative to NB regression, particularly when the number of events is low or when numerical stability is a concern.

翻译：计数终点在临床试验中十分常见，尤其适用于低血糖等复发性事件。当研究重点在于比较治疗组间的总体事件发生率时，负二项式回归因其能处理过度离散性且仅需事件计数和暴露时间而被广泛使用。然而，当事件稀疏时，负二项式回归可能在数值上不稳定，且通过基线协变量调整获得的效率增益可能对模型设定错误较为敏感。我们提出一种经验方法，该方法以与负二项式回归相同的边际估计目标——边际事件发生率之比——为目标，同时避免对计数结果做出分布假设。模拟研究表明，该经验方法在各种场景下（包括极端过度离散和零膨胀）均能保持适当的I类错误控制，达到与负二项式回归相当的检验效能，并能通过基线协变量调整获得一致的效率增益。我们通过使用QWINT-5试验中比较艾夫西托拉阿尔法胰岛素与德谷胰岛素在成人1型糖尿病患者中的严重低血糖数据来阐述该方法。在此稀疏事件场景下，经验方法产生了稳定的边际发生率估计和与观察到的发生率高度一致的发生率比，而负二项式回归则在最稀疏的区间内表现出更高的敏感性和与观察到的发生率更大的偏差。所提出的经验方法为负二项式回归提供了一种稳健且数值稳定的替代方案，尤其适用于事件数量较少或数值稳定性受到关注的情况。