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

使用事件历史模型中的上下文和个体级别的时间相关协变量进行推理

• 批准号: 9811983
• 负责人: Susan Murphy
• 金额: $ 6.42万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-01-01 至 1999-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9811983&HistoricalAwards=false
• 关键词: Inference; Contextual; Individual; Level; Time

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.

社会科学家使用事件历史模型来了解许多生活事件的持续时间和时间变化的原因，例如贫困期的持续时间、退休时间、婚前生育时间、开始滥用药物的时间等。内生时间依赖性协变量（例如政府支持量和针对个人的干预计划）可以用来解释这种变化。 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。 当时间相关协变量和持续时间或时间都是共同原因的结果时，可能会发生混杂。 在这种情况下，时间相关协变量可以与持续时间或定时的变化相关联，但可能不会引起变化。 然而，在设计干预计划或社会计划时，了解持续时间/时间变化的原因非常重要。 为了确定由时间相关协变量引起的持续时间/时间变化的比例，必须控制混杂。 然而，对时间相关协变量对结果的影响的混杂进行适当调整需要非常仔细的考虑。 事实上，通过将混杂因素纳入事件历史分析模型来控制混杂因素的传统方法通常只会引入更多混杂因素。 该项目将（1）应用实验视角来解决有关时间依赖性协变量对持续时间/时间的影响的问题，（2）说明测量时间依赖性协变量的影响所固有的混杂问题，（3）说明如何消除混杂，以及（4）开发研究方法来消除时间依赖性上下文协变量对多级模型中危险率的影响的混杂。 这项研究得到了职业中期方法学机会奖学金公告下的方法论、测量和统计项目以及统计和概率项目的支持。