Inference With Contextual and Individual Level Time-Dependent Covariates in Event History Models

社会科学家使用事件历史模型，以了解许多生活事件的持续时间和时间变化的原因，如贫困的持续时间，退休的时间，婚前生育的时间，药物滥用开始的时间等。 由于内源性时间依赖性协变量的值可能部分由受试者决定或选择，因此调整混杂因素很重要。 当时间依赖性协变量和持续时间或时间都是共同原因的结果时，可能会发生混淆。 在这种情况下，时间依赖协变量可能与持续时间或时间的变化相关联，但可能不会引起变化。 然而，在设计干预方案或社会方案时，重要的是要了解持续时间/时间变化的原因。 为了确定时间依赖性协变量引起的持续时间/时间变异的比例，必须控制混杂。 然而，时间依赖性协变量对结果影响的混杂效应的适当调整需要非常仔细的考虑。 事实上，通过在事件历史分析模型中包括混杂因素来控制混杂的传统方法通常只会引入更多的混杂。 这个项目将（1）应用实验的角度来研究时间依赖性协变量对持续时间/时间的影响，（2）说明测量时间依赖性协变量影响时固有的混淆问题，（3）说明如何消除混淆，以及（4）发展研究方法，以消除多水平模型中时间相关背景协变量对风险率影响的混杂。 这项研究得到了方法，测量和统计计划以及职业中期方法学机会奖学金公告下的统计和概率计划的支持。

Social scientists use event history models in order the understand the causes of variation in the duration and timing of many life events such as the duration of poverty spells, timing of retirement, timing of premarital birth, timing of drug abuse initiation, etc. An endogenous time-dependent covariate such as amount of governmental support and person-tailored intervention programs may be used to explain this variation. Since the value of the endogenous time-dependent covariate may be partially determined or selected by the subject, it is important to adjust for confounding. Confounding may occur when both the time-dependent covariate and the duration or timing are outcomes of a common cause. In this case the time-dependent covariate may be associated with the variation in the duration or timing, yet may not cause the variation. Yet in designing intervention programs or social programs it is important to understand the causes of the variation in duration/timing. In order to ascertain the proportion of variation in duration/timing caused by time-dependent covariate, confounding must be controlled. However, proper adjustment for confounding of the effect of a time-dependent covariate on the outcome needs very careful thought. Indeed, the traditional approach of controlling for confounding by including the confounder in the event history analysis model will often only introduce more confounding. This project will (1) apply an experimental perspective to questions concerning the effect of a time-dependent covariate on duration/timing, (2) illustrate the confounding issues inherent in measuring the effects of time-dependent covariates, (3) illustrate how confounding may be eliminated, and (4) develop research methodology to eliminate confounding of the effect of time-dependent contextual covariates on hazard rates in multilevel models. This research is supported by the Methodology, Measurement, and Statistics Program and the Statistics and Probability Program under the Mid-Career Methodological Opportunities Fellowship Announcement.