ABSTRACT

Hypoglycemia is a major safety concern for diabetic patients. Hypoglycemic events can be modeled based on time to recurrent events or count data. In this article, we evaluated a gamma frailty model with variance estimated by the inverse of observed Fisher information matrix, a gamma frailty model with the sandwich variance estimator, and a piecewise negative binomial regression model. Simulations showed that the sandwich variance estimator performed better when the frailty model is mis-specified, and the piecewise negative binomial regression sometimes fails to converge. All three methods were applied to a dataset from a clinical trial evaluating insulin treatments.

摘要

低血糖是糖尿病患者的主要安全问题。低血糖事件可以基于复发事件的时间或计数数据来建模。在本文中，我们评估了一个方差由观察到的 Fisher 信息矩阵的逆估计的 gamma 脆弱模型、一个具有三明治方差估计器的 gamma 脆弱模型和一个分段负二项式回归模型。模拟表明，当错误指定脆弱模型时，三明治方差估计器的性能更好，并且分段负二项式回归有时无法收敛。所有三种方法都应用于评估胰岛素治疗的临床试验的数据集。