Estimation of the complier causal hazard ratio under dependent censoring
依赖删失下服从者因果风险比的估计AI 解读 ↓
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依赖删失下服从者因果风险比的估计
- 研究问题
- 当二元处理内生，且持续时间与右删失时间在控制协变量后仍相关时，如何识别和估计服从者群体中的因果风险比？
- 方法与思路
- 作者使用二元工具变量，在服从者层设定比例风险模型、参数化删失模型及参数 copula，并以估计服从者概率进行加权似然估计。
- 材料陈述的贡献
- 摘要称，本文给出新的可识别条件、两步估计程序及渐近性质，并开展模拟及就业培训、乳腺癌筛查两项应用。
- 阅读关注点
- 应核对工具变量和服从者定义、联合删失模型的识别条件，以及第一步概率估计如何影响第二步风险比估计和推断。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收。摘要未列出完整识别条件，风险比的结论依赖所设持续时间与删失模型。
原文摘要
In this work, we are interested in studying the causal effect of an endogenous binary treatment on a dependently censored duration outcome. By dependent censoring, it is meant that the duration time ($T$) and right censoring time ($C$) are not statistically independent of each other, even after conditioning on the measured covariates. The endogeneity issue is handled by making use of a binary instrumental variable for the treatment. To deal with the dependent censoring problem, it is assumed that on the stratum of compliers: (i) $T$ follows a semiparametric proportional hazards model; (ii) $C$ follows a fully parametric model; and (iii) the relation between $T$ and $C$ is modeled by a parametric copula, such that the association parameter can be left unspecified. In this framework, the treatment effect of interest is the complier causal hazard ratio (CCHR). We devise an estimation procedure that is based on a weighted maximum likelihood approach, where the weights are the probabilities of an observation coming from a complier. The weights are estimated non-parametrically in a first stage, followed by the estimation of the CCHR. Novel conditions under which the model is identifiable are given, a two-step estimation procedure is proposed and some important asymptotic properties are established. Simulations are used to assess the validity and finite-sample performance of the estimation procedure. Finally, we apply the approach to estimate the CCHR of both job training programs on unemployment duration and periodic screening examinations on time until death from breast cancer. The data come from the National Job Training Partnership Act study and the Health Insurance Plan of Greater New York experiment respectively.