使用SPACE程序从复杂调查数据中估计多状态生命表函数及其变异性。
Estimation of multi-state life table functions and their variability from complex survey data using the SPACE Program.
作者信息
Cai Liming, Hayward Mark D, Saito Yasuhiko, Lubitz James, Hagedorn Aaron, Crimmins Eileen
机构信息
Office of Analysis and Epidemiology, National Center for Health Statistics. 3311 Toledo Road, room 6330, Hyattsville, MD 20782;
出版信息
Demogr Res. 2010 Jan 26;22(6):129-158. doi: 10.4054/DemRes.2010.22.6.
Abstract
The multistate life table (MSLT) model is an important demographic method to document life cycle processes. In this paper, we present the SPACE (Stochastic Population Analysis for Complex Events) program to estimate MSLT functions and their sampling variability. It has several advantages over other programs, including the use of micro-simulation and the bootstrap method to estimate the variance of MSLT functions. Simulation enables researchers to analyze a broader array of statistics than the deterministic approach, and may be especially advantageous in investigating distributions of MSLT functions. The bootstrap method takes sample design into account to correct the potential bias in variance estimates.
摘要
多状态生命表（MSLT）模型是记录生命周期过程的一种重要人口统计学方法。在本文中，我们介绍了用于估计MSLT函数及其抽样变异性的SPACE（复杂事件的随机人口分析）程序。它相对于其他程序具有几个优势，包括使用微观模拟和自助法来估计MSLT函数的方差。与确定性方法相比，模拟使研究人员能够分析更广泛的统计数据，并且在研究MSLT函数的分布时可能特别有利。自助法考虑了样本设计，以纠正方差估计中的潜在偏差。
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