Department of Public Health, Erasmus MC, University Medical Centre Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands.
Am J Epidemiol. 2012 Nov 15;176(10):929-37. doi: 10.1093/aje/kws152. Epub 2012 Oct 16.
There are several measures that summarize the mortality experience of a population. Of these measures, life expectancies are generally preferred based on their simpler interpretation and direct age standardization, which makes them directly comparable between different populations. However, traditional life expectancy estimations are highly inaccurate for smaller populations and consequently are seldom used in small-area applications. In this paper, the authors compare the relative performance of traditional life expectancy estimation with a Bayesian random-effects approach that uses correlations (i.e., borrows strength) between different age groups, geographic areas, and sexes to improve the small-area life expectancy estimations. In the presented Monte Carlo simulations, the Bayesian random-effects approach outperforms the traditional approach in terms of bias, root mean square error, and coverage of the 95% confidence intervals. Moreover, the Bayesian random-effects approach is found to be usable for populations as small as 2,000 person-years at risk, which is considerably smaller than the minimum of 5,000 person-years at risk recommended for the traditional approach. As such, the proposed Bayesian random-effects approach is well-suited for estimation of life expectancies in small areas.
有几种方法可以总结一个人群的死亡率。在这些方法中，预期寿命通常是首选，因为它们的解释更简单，并且可以直接进行年龄标准化，这使得它们可以在不同人群之间直接比较。然而，传统的预期寿命估计对于较小的人群来说非常不准确，因此很少在小区域应用中使用。在本文中，作者比较了传统的预期寿命估计方法与贝叶斯随机效应方法的相对性能，贝叶斯随机效应方法利用了不同年龄组、地理区域和性别的相关性（即借用强度）来提高小区域的预期寿命估计。在提出的蒙特卡罗模拟中，贝叶斯随机效应方法在偏差、均方根误差和 95%置信区间的覆盖度方面优于传统方法。此外，还发现贝叶斯随机效应方法适用于风险人群小至 2000 人年的情况，这比传统方法建议的风险人群小至 5000 人年小得多。因此，所提出的贝叶斯随机效应方法非常适合小区域的预期寿命估计。
