标题： 半参数筛极大似然估计在治愈模型下部分区间删失和左截断数据的应用——以自然流产数据为例 显示英文标题

标题： Semiparametric Sieve Maximum Likelihood Estimation Under Cure Model with Partly Interval Censored and Left Truncated Data for Application to Spontaneous Abortion Data

摘要： 这项研究的动机来源于涉及自然流产（SAB）结局的妊娠观察性研究。显然，一些女性经历了SAB事件，而另一些则没有。此外，由于招募孕妇进入这些研究的方式，数据存在左删失。对于那些经历SAB的女性，她们的确切事件时间有时未知。最后，在怀孕期间，一小部分女性会失访。所有这些因素导致数据存在左删失、部分区间删失和右删失，并且有一个明确定义的治愈部分。我们考虑非混合Cox回归治愈率模型，并采用半参数基于样条的筛极大似然方法来分析此类数据。利用现代经验过程理论，我们证明了筛估计量的参数部分和非参数部分都具有一致性，并建立了两部分的渐近正态性。进行了模拟研究以确定有限样本性能。最后，我们将我们的方法应用于自发性流产观察性研究的数据库。 显示英文摘要

摘要： This work was motivated by observational studies in pregnancy with spontaneous abortion (SAB) as outcome. Clearly some women experience the SAB event but the rest do not. In addition, the data are left truncated due to the way pregnant women are recruited into these studies. For those women who do experience SAB, their exact event times are sometimes unknown. Finally, a small percentage of the women are lost to follow-up during their pregnancy. All these give rise to data that are left truncated, partly interval and right-censored, and with a clearly defined cured portion. We consider the non-mixture Cox regression cure rate model and adopt the semiparametric spline-based sieve maximum likelihood approach to analyze such data. Using modern empirical process theory we show that both the parametric and the nonparametric parts of the sieve estimator are consistent, and we establish the asymptotic normality for both parts. Simulation studies are conducted to establish the finite sample performance. Finally, we apply our method to a database of observational studies on spontaneous abortion.