The Cox proportional frailty model with a random effect has been proposed for the analysis of right-censored data which consist of a large number of small clusters of correlated failure time observations. For right-censored data, Cai et al. 3 proposed a class of semiparametric mixed-effects models which provides useful alternatives to the Cox model. We demonstrate that the approach of Cai et al. 3 can be used to analyze clustered doubly censored data when both left- and right-censoring variables are always observed. The asymptotic properties of the proposed estimator are derived. A simulation study is conducted to investigate the performance of the proposed estimator.
Related Content
Bayesian semiparametric mixture Tobit models with left censoring, skewness, and covariate measurement errors
Common problems to many longitudinal HIV/AIDS, cancer, vaccine, and environmental exposure studies are the presence of a lower limit of quantification of an outcome with skewness and time-varying covariates with measurement errors. There has been relatively little work published simultaneously de...


Semiparametric regression analysis for clustered doubly-censored data
This paper considers clustered doubly-censored data that occur when there exist several correlated survival times of interest and only doubly censored data are available for each survival time. In this situation, one approach is to model the marginal distribution of failure times using semiparame...
Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
We introduce a quantile-adaptive framework for nonlinear variable screening with high-dimensional heterogeneous data. This framework has two distinctive features: (1) it allows the set of active variables to vary across quantiles, thus making it more flexible to accommodate heterogeneity; it is m...


A Bayesian semiparametric model for bivariate sparse longitudinal data
Mixed-effects models have recently become popular for analyzing sparse longitudinal data that arise naturally in biological, agricultural and biomedical studies. Traditional approaches assume independent residuals over time and explain the longitudinal dependence by random effects. However, when ...
Regression analysis using dependent Polya trees
Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior ...

Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
This paper concerns robust inference on average treatment effects followingmodel selection. In the selection on observables framework, we show how toconstruct confidence intervals based on a doubly-robust estimator that arerobust to model selection errors and prove that they are valid uniformly o...
Simultaneous Bayesian Inference for Skew-Normal Semiparametric Nonlinear Mixed-Effects Models with Covariate Measurement Errors
Longitudinal data arise frequently in medical studies and it is a com- mon practice to analyze such complex data with nonlinear mixed-effects (NLME) models which enable us to account for between-subject and within-subject variations. To partially explain the variations, covariates are usually int...

Semiparametric hazard function estimation in meta-analysis for time to event data
Meta-analyses have been widely used to combine information from survival data using estimated parameters in, for example, a Cox model. A number of approaches dealing with study level random effects have been developed. However, there are far fewer meta-analysis approaches for estimating survival ...
Identifying genetic marker sets associated with phenotypes via an efficient adaptive score test
In recent years, genome-wide association studies (GWAS) and gene-expression profiling have generated a large number of valuable datasets for assessing how genetic variations are related to disease outcomes. With such datasets, it is often of interest to assess the overall effect of a set of genet...

Small area semiparametric additive monotone models
In this paper, semiparametric monotone mixed models are introduced, exploring, in particular, the problems of estimation and bootstrapping. The models are defined in a small area setting, using the assumption that some of the auxiliary variables have a monotone relationship with the response, and...