ESTIMATES OF ABSOLUTE CAUSE-SPECIFIC RISK IN COHORT STUDIES
ESTIMATES OF ABSOLUTE CAUSE-SPECIFIC RISK IN COHORT STUDIES
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DOI：
10.2307/2532098
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发表时间：
1990-09-01
期刊：
影响因子：
1.9
通讯作者：
GAIL, MH
中科院分区：
文献类型：
--
作者：
BENICHOU, J;GAIL, MH
在本文中，我们研究了估计时间间隔 [t1，t2] 内事件 c1 的绝对风险的方法，假设个体在 t1 时处于风险之中并且存在竞争风险。我们讨论了测量个体患者预后的绝对风险的一些优点以及比较两个治疗组的解释的一些困难。我们还讨论了绝对风险概念在评估预防疾病的公共卫生措施中的重要性。 方差计算允许人们衡量随机误差和系统误差在估计绝对风险时的相对重要性。还进行了效率计算，以确定使用非参数方法或使用灵活的分段指数模型而不是简单的指数模型来估计绝对风险时损失了多少精度，并且其他计算表明了当简单指数模型无效时，简单指数模型产生的偏差程度。这样的计算表明更灵活的模型在实践中将是有用的。模拟证实，无症状方法可以在实际大小的样本中产生可靠的方差估计和置信区间覆盖。
In this paper we study methods for estimating the absolute risk of an event c1 in a time interval [t1, t2], given that the individual is at risk at t1 and given the presence of competing risks. We discuss some advantages of absolute risk for measuring the prognosis of an individual patient and some difficulties of interpretation for comparing two treatment groups. We also discuss the importance of the concept of absolute risk in evaluating public health measures to prevent disease. Variance calculations permit one to gauge the relative importance of random and systemic errors in estimating absolute risk. Efficiency calculations were also performed to determine how much precision is lost in estimating absolute risk with a nonparametric approach or with a flexible piecewise exponential model rather than a simple exponential model, and other calculations indicate the extent of bias that arises with the simple exponential model when that model is invalid. Such calculations suggest that the more flexible models will be useful in practice. Simulations confirm that asymptomic methods yield reliable variance estimates and confidence interval coverages in samples of practical size.