带信息观测的与时间相关协变量的面板计数数据分析

面板计数数据在许多临床和观察性研究中十分常见,在某些情况下,观测过程是带信息的.文章提出了一个新的联合模型来分析带与时间相关协变量的面板计数数据,其中可能存在带两个潜在变量的信息观测过程.对于所提模型的推断,建立了一类估计方程,得到的估计量经证明是相合的和渐近正态的.最后通过数值模拟评估了文章中所提估计的有限样本性质,...     

面板数据下带固定效应的线性回归模型的稳健变量选择

本文结合复合分位数回归和自适应LASSO惩罚方法为固定效应面板数据模型提供了一种稳健变量选择过程。先通过正向正交偏差变换消除固定效应,再利用自适应LASSO构造惩罚复合分位数回归目标函数,进而同时进行回归系数的估计和变量选择。在一些正则条件下,证明了所提出的估计具有Orcale性质。该方法不仅消除了固定效应对估计的影响,而且具有稳健性。模拟研究了所提出方法的有限样本性质并将其应用于实际数据分析。     

带有信息的区间删失数据下AFT模型的参数估计

在生存分析研究中,多数文章假定感兴趣的失效时间和删失时间是独立的,但这一假设在实际情况中未必合理。如果忽略失效时间与删失时间的相依性,可能会导致错误的结论。所以本文考虑在带有信息的K型区间删失数据下,采用基于两步估计的极大似然估计方法对误差项服从标准正态分布的加速失效时间模型(accelerated failure time model,AFT)进行参数估计。同时还进行了数值模拟以验证提出方法的有效性。最后,应用所提出的方法分析艾滋病的临床试验数据。     

有偏抽样下带终止时间和带信息观察时间的纵向数据分析

纵向数据经常出现在生物医学等实际领域,而且在许多情况下,观察时间和终止时间是带信息的,同时可能会存在有偏抽样.本文在有偏抽样下,对于带信息观察时间和终止时间的纵向数据,提出了一个联合建模方法,并利用借代力量方法给出了模型参数的估计,同时获得了这些估计的渐近性质.最后,提出一种模型检验方法.     

Semiparametric analysis of longitudinal data with informative observation times

In many longitudinal studies,observation times as well as censoring times may be correlated with longitudinal responses.This paper considers a multiplicative random effects model for the longitudinal response where these correlations may exist and a joint modeling approach is proposed via a shared latent variable.For inference about regression parameters,estimating equation approaches are developed and asymptotic properties of the proposed estimators are established.The finite sample behavior of the methods is examined through simulation studies and an application to a data set from a bladder cancer study is provided for illustration.     

Smoothing estimation of rate function for recurrent event data with informative censoring

This paper proposes kernel estimation of the occurrence rate function for recurrent event data with informative censoring. An informative censoring model is considered with assumptions made on the joint distribution of the recurrent event process and the censoring time without modeling the censoring distribution. Under the validity of the informative censoring model, we also show that an estimator based on the assumption of independent censoring becomes inappropriate and is generally asymptotically biased. To investigate the asymptotic properties of the proposed estimator, the explicit form of its asymptotic mean squared risk and the asymptotic normality are derived. Meanwhile, the empirical consistent smoothing estimator for the variance function of the estimator is suggested. The performance of the estimators are also studied through Monte Carlo simulations. An epidemiological example of intravenous drug user data is used to show the influence of informative censoring in the estimation of the occurrence rate functions for inpatient cares over time.     

Analysis of panel count data with time-dependent covariates and informative observation process

Panel count data occur in many clinical and observational studies and in some situations the observation process is informative. In this article, we propose a new joint model for the analysis of panel count data with time-dependent covariates and possibly in the presence of informative observation process via two latent variables. For the inference on the proposed model, a class of estimating equations is developed and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a lack-of-fit test is provided for assessing the adequacy of the model. The finite-sample behavior of the proposed methods is examined through Monte Carlo simulation studies which suggest that the proposed approach works well for practical situations. Also an illustrative example is provided.     

Exponential family non-linear models for categorical data with errors of observation

We present a general framework for treating categorical data with errors of observation. We show how both latent class models and models for doubly sampled data can be treated as exponential family nonlinear models. These are extended generalized linear models with the link function substituted by an observationwise defined non-linear function of the model parameters. The models are formulated in terms of structural probabilities and conditional error probabilities, thus allowing natural constraints when modelling errors of observation. We use an iteratively reweighted least squares procedure for obtaining maximum likelihood estimates. This is faster than the traditionally used EM algorithm and the computations can be made in GLIM.¹ As examples we analyse three sets of categorical data with errors of observation which have been analysed before by Ashford and Sowden,² Goodman³ and Chen,⁴ respectively.