Greenland S
Department of Epidemiology, UCLA School of Public Health 90024-1772.
Stat Med. 1991 Jul;10(7):1069-74. doi: 10.1002/sim.4780100707.
Although the traditional unrestricted ('non-parametric') estimators of directly standardized rates and rate differences remain unbiased in sparse data, they tend to suffer from instability (low precision). As a result, many authors have proposed more precise estimators based on parametric models for the rates. This paper provides a general approach for constructing estimators of standardized parameters using generalized linear models, and shows that, in some common special cases, these model-based ('smoothed') estimators can have an exceptionally simple form.
尽管直接标准化率和率差的传统无限制（“非参数”）估计量在稀疏数据中仍保持无偏性，但它们往往存在不稳定性（低精度）。因此，许多作者提出了基于率的参数模型的更精确估计量。本文提供了一种使用广义线性模型构建标准化参数估计量的通用方法，并表明，在一些常见的特殊情况下，这些基于模型的（“平滑”）估计量可以具有非常简单的形式。
