Improved generalized estimating equation analysis via xtqls for quasi-least squares in Stata
Improved generalized estimating equation analysis via xtqls for quasi-least squares in Stata
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DOI：
10.1177/1536867x0700700201
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发表时间：
2007-01-01
期刊：
影响因子：
4.8
通讯作者：
Leonard, Mary
中科院分区：
文献类型：
--
作者：
Shults, Justine;Ratcliffe, Sarah J.;Leonard, Mary
拟最小二乘法 (QLS) 是在广义估计方程 (GEE) 方法框架内估计相关参数的另一种方法，用于分析相关的横截面和纵向数据。本文总结了几篇报告中发生的 QLS 开发，并描述了它与 Stata 中用户编写的程序 xtqls 的使用。此外，它还展示了 QLS 的以下优点：（1）QLS 允许一些尚未在 GEE 框架中实现的相关结构，（2）如果 GEE 估计不可行，QLS 可以用作 GEE 的替代方案，（3）QLS 使用与 GEE 相同的估计方程来估计 beta；因此，QLS 可以涉及 GEE 已有的项目。特别是，xtqls 在迭代方法中调用 Stata 程序 xtgee，该方法交替更新相关参数 a 的估计，然后使用 xtgee 在当前 a 估计下求解 Beta 的 GEE。这种方法的好处是，在 xtqls 之后，用户可以轻松使用所有常用的回归后估计命令。
Quasi-least squares (QLS) is an alternative method for estimating the correlation parameters within the framework of the generalized estimating equation (GEE) approach for analyzing correlated cross-sectional and longitudinal data. This article summarizes the development of QLS that occurred in several reports and describes its use with the user-written program xtqls in Stata. Also, it demonstrates the following advantages of QLS: (1) QLS allows some correlation structures that have not yet been implemented in the framework of GEE, (2) QLS can be applied as an alternative to GEE if the GEE estimate is infeasible, and (3) QLS uses the same estimating equation for estimation of beta as GEE; as a result, QLS can involve programs already available for GEE. In particular, xtqls calls the Stata program xtgee within an iterative approach that alternates between updating estimates of the correlation parameter a and then using xtgee to solve the GEE for beta at the current estimate of a. The benefit of this approach is that after xtqls, all the usual postregression estimation commands are readily available to the user.