Some Latent-Class Models for Controlling for Selection Bias in Covariate Effects in Event History Analysis: A Project Description

该项目旨在开发新的事件历史模型组，这些模型控制事件发生风险率协变量状态内外的选择偏倚，并评估协变量对事件发生的净效应。 协变量影响的选择偏差-例如，男子结婚是否会导致失业率下降，或者在某一年龄结婚并保持婚姻的男子的不受控制的特征是否会导致失业率下降，或者在婚姻中使用大麻是否会导致离婚率的增加，或者成为大麻使用者并保留这种行为的人的不受控制的特征，结婚预示着更高的离婚率--这是分析非实验数据以建立因果关系的一个核心问题。 虽然其他人最近引入了一些事件历史的方法/模型来处理这个问题，这个项目将开发不同的模型组来处理这个问题的各个方面，特别是，双变量危险及其扩展的移动-停留模型，对数线性潜在类模型及其扩展。 第二个相关目标是澄清新模型参数估计的可靠性和稳定性的特点，以及可靠性和稳定性对指定模型的替代假设的依赖性。（2）控制选择偏差的基本对数线性潜类模型的阐述和扩展;（3）这些模型在社会科学研究中的应用;（4）研究参数估计的稳定性和可靠性，作为模型中所作具体假设的函数;以及（5）将项目中开发的模型/方法的结果与相关的现有替代模型/方法的结果进行比较。

This project aims to develop new groups of event-history models which control for selection bias in and out of the states of a covariate of the hazard rate of event occurrence, and assess the net effect of the covariate on event occurrence. Selection bias in the effects of covariates -- such as whether men's entry into marriage causes a decrease in the rate of unemployment or rather uncontrolled characteristics of men who become and remain married at a given age predict a lower rate of unemployment, or whether marijuana use in marriage causes an increase in the rate of divorce or uncontrolled characteristics of people who become marijuana users and retain this behavior in marriage predict a higher rate of divorce -- is a central issue in the analysis of nonexperimental data in order to establish causality. Although others have recently introduced some event-history methods/models to handle this problem, this project will develop distinct groups of models to handle aspects of this issue; in particular, mover-stayer models for bivariate hazards and their extensions, and loglinear latent-class models and their extensions. As a related second aim, the project will clarify the characteristics of the new models regarding the reliability and stability of their parameter estimates and the dependence of the reliability and stability on alternative assumptions made to specify the models.The project has five components: (1) development of software for the new mover-stayer models; (2) an elaboration and extension of basic loglinear latent-class models for controlling for selection bias; (3) applications of those models in social science research; (4) an investigation of the stability and reliability of parameter estimates as a function of specific assumptions made in the models; and (5) some comparisons of the results from the models/methods to be developed in the project with those from related existing alternative models/methods.