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Bivariate interval-censored failure time data occurs in many fields such as medical followup studies. In this article, we propose an estimation procedure for the association between two related interval-censored survival times when they follow a copula model jointly and the additive hazards model marginally. A simulation study was conducted for assessing the finite sample performance of the presented method and suggests that it works well in practical situations. The proposed method is applied to the AIDS Clinical Trial Group (ACTG) 181 data that motivated this research. [ABSTRACT FROM AUTHOR]

A large literature has been established for random survival forest (RSF), a popular tool developed to analyze right-censored failure time data, under various situations. However, its prediction performance may not be optimal sometimes. To address this issue, we propose two optimal model averaging methods based on martingale residual processes. In particular, an in-of-bag and out-of-bag (IBOB) data process is defined, and two new IBOB functionals criteria are derived for the selection of weights. Furthermore, for their implementation, a greedy algorithm is presented, and the asymptotic optimality of the proposed model averaging approaches is established along with the convergence of the greedy averaging algorithms. Finally, an extensive simulation study is conducted, which indicates that the proposed methods work well, and an illustration is provided. [ABSTRACT FROM AUTHOR]

To test pleiotropy based on longitudinal data, two main difficulties are the construction of test statistics and the formulation of decision-making criteria. In particular, the commonly used log-likelihood ratio test is not available since there does not exist a likelihood function and the estimation is based on estimating equations in general. To address this and construct a statistic similar to the -2 log-likelihood ratio test statistic, we propose a new concept, a complex log-likelihood function, such that its first derivative is equal to a given estimating function. In consequence, the -2 complex log-likelihood ratio test is constructed for testing genetic pleiotropy, and the validity of the test procedure is established. An extensive simulation study is performed, which suggests that the proposed method works well for practical situations. Furthermore, it is applied to a set of longitudinal data on tropical storms. [ABSTRACT FROM AUTHOR]

Interval-censored failure time data frequently occur in many areas and a great deal of literature on their analyses has been established. In this article, we discuss the situation where one faces bivariate interval-censored data arising from case-cohort studies, which are commonly used as a tool to save costs when disease incidence is low and covariates are difficult to obtain. For this problem, a class of copula-based semi-parametric models is presented and for estimation, a sieve weighted maximum likelihood estimation procedure is developed. The resulting estimators of regression parameters are shown to be strongly consistent and asymptotically normal. Furthermore, the proposed method is generalized to the situation of non rare diseases. A simulation study is conducted to assess the finite sample performance of the proposed method and suggests that it performs well in practice. [ABSTRACT FROM AUTHOR]

Estimation of causal treatment effects has attracted a great deal of interest in many areas including social, biological and health science, and for this, instrumental variable (IV) has become a commonly used tool in the presence of unmeasured confounding. In particular, many IV methods have been developed for right-censored time-to-event outcomes. In this paper, we consider a much more complicated situation where one faces interval-censored time-to-event outcomes, which are ubiquitously present in studies with, for example, intermittent follow-up but are challenging to handle in terms of both theory and computation. A sieve maximum likelihood estimation procedure is proposed for estimating complier causal treatment effects under the additive hazards model, and the resulting estimators are shown to be consistent and asymptotically normal. A simulation study is conducted to evaluate the finite sample performance of the proposed approach and suggests that it works well in practice. It is applied to a breast cancer screening study. [ABSTRACT FROM AUTHOR]

Clustered interval-censored failure time data occur in many areas and many methods have been proposed for their analysis. In practice, in addition to the clustering and interval censoring, such data may also present two other issues, cure fraction and informative cluster size, and it does not seem to exist an approach that can deal with all of these issues together. To address this, we present a within-cluster resampling method and in the approach, the multiple imputation procedure is applied for estimation of unknown parameters. To assess the performance of the proposed method, a simulation study is conducted and suggests that it works well in practical situations. Also the method is applied to a set of real data that motivated this study. [ABSTRACT FROM AUTHOR]

The additive hazards model is one of the commonly used models for failure time data analysis and many authors have discussed its estimation under various situations. In this paper, we consider the same problem but under some inequality constraints when one faces current status data, for which it does not seem to exist an established estimation procedure due to the difficulties involved. In particular, the restricted maximum likelihood estimation approach is derived and the asymptotic properties of the resulting estimator of regression parameters are established with the use of some optimization methods. Simulation studies are conducted to assess the finite-sample performance of the proposed method and suggest that it works well for practical situations. A real data application is provided to demonstrate the utility of the proposed approach. [ABSTRACT FROM AUTHOR]

The gap time between recurrent events is often of primary interest in many fields such as medical studies, and in this article, we discuss regression analysis of the gap times arising from a general class of additive transformation models. For the problem, we propose two estimation procedures, the modified within-cluster resampling (MWCR) method and the weighted risk-set (WRS) method, and the proposed estimators are shown to be consistent and asymptotically follow the normal distribution. In particular, the estimators have closed forms and can be easily determined, and the methods have the advantage of leaving the correlation among gap times arbitrary. A simulation study is conducted for assessing the finite sample performance of the presented methods and suggests that they work well in practical situations. Also the methods are applied to a set of real data from a chronic granulomatous disease (CGD) clinical trial. [ABSTRACT FROM AUTHOR]

Failure time data occur in many areas and also in various forms and in particular, many authors have discussed regression analysis of failure time data in the presence of interval censoring, a cured subgroup or mismeasured covariates. However, it does not seem to exist an established procedure that can deal with all three issues together. Corresponding to this, we propose a sieve maximum likelihood estimation procedure that takes into account all three issues with the use of the SIMEX algorithm. The asymptotic properties of the proposed estimators are established, and an extensive simulation study is also conducted and suggests that the proposed method works well for practical situations. [ABSTRACT FROM AUTHOR]

The testing of the stratum effects in the Cox model is an important and commonly asked question in medical research as well as in many other fields. In this paper, we will discuss the problem where one observes interval-censored failure time data and generalize the procedure given in Sun and Yang (2000) for right-censored data. The asymptotic distribution of the new test statistic is established and the simulation study conducted for the evaluation of the finite sample properties of the method suggests that the generalized procedure seems to work well for practical situations. An application is provided. [ABSTRACT FROM AUTHOR]

Interval-censored data naturally arise in many studies. For their regression analysis, many approaches have been proposed under various models and for most of them, the inference is carried out based on the asymptotic normality. In particular, Zhang et al. (2005) discussed the procedure under the linear transformation model. It is well-known that the symmetric property implied by the normal distribution may not be appropriate sometimes. Also the method could underestimate the variance of estimated parameters. This paper proposes an empirical likelihood-based procedure for the problem. Simulation and the analysis of a real data set are conducted to assess the performance of the procedure. [ABSTRACT FROM PUBLISHER]

In recent years the analysis of interval-censored failure time data has attracted a great deal of attention and such data arise in many fields including demographical studies, economic and financial studies, epidemiological studies, social sciences, and tumorigenicity experiments. This is especially the case in medical studies such as clinical trials. In this article, we discuss regression analysis of one type of such data, Case I interval-censored data, in the presence of left-truncation. For the problem, the additive hazards model is employed and the maximum likelihood method is applied for estimations of unknown parameters. In particular, we adopt the sieve estimation approach that approximates the baseline cumulative hazard function by linear functions. The resulting estimates of regression parameters are shown to be consistent and efficient and have an asymptotic normal distribution. An illustrative example is provided. [ABSTRACT FROM AUTHOR]

Recently, there has been a great interest in the analysis of longitudinal data in which the observation process is related to the longitudinal process. In literature, the observation process was commonly regarded as a recurrent event process. Sometimes some observation duration may occur and this process is referred to as a recurrent episode process. The medical cost related to hospitalization is an example. We propose a conditional modeling approach that takes into account both informative observation process and observation duration. We conducted simulation studies to assess the performance of the method and applied it to a dataset of medical costs. [ABSTRACT FROM PUBLISHER]

Multivariate panel count data often occur when there exist several related recurrent events or response variables defined by occurrences of related events. For univariate panel count data, several nonparametric treatment comparison procedures have been developed. However, it does not seem to exist a nonparametric procedure for multivariate cases. Based on differences between estimated mean functions, this article proposes a class of nonparametric test procedures for multivariate panel count data. The asymptotic distribution of the new test statistics is established and a simulation study is conducted. Moreover, the new procedures are applied to a skin cancer problem that motivated this study. [ABSTRACT FROM AUTHOR]

Linear transformation models provide a class of flexible models for regression analysis of failure time data and several methods have been proposed for fitting them to right-censored failure time data (Chen, 2002; Cheng et al., 1995; Yin and Zeng, 2006). This article considers the fitting of these models to current status data, a special type of interval-censored failure time data. We derive the maximum likelihood estimates and their information bound and establish the efficiency of the estimates. Simulation studies show that the approach is appropriate for practical use and an application is given to illustrate the approach. [ABSTRACT FROM AUTHOR]

New drug discovery in the pediatrics has dramatically improved survival, but with long- term adverse events. This motivates the examination of adverse outcomes such as long-term toxicity in a phase IV trial. An ideal approach to monitor long-term toxicity is to systematically follow the survivors, which is generally not feasible. Instead, cross-sectional surveys are conducted in Hudson et al. (2007), with one of the objectives to estimate the cumulative incidence rates along with specific interest in fixed-term (5 or 10 year) rates. We present inference procedures based on current status data to our motivating example with very interesting findings. [ABSTRACT FROM AUTHOR]

This article discusses regression analysis of current status data, which occur in many fields including cross-sectional studies, demographical investigations, and tumorigenicity experiments (Keiding, 1991; Sun 2006). For the problem, we focus on the situation where the survival time of interest can be described by the additive hazards model and a multiple imputation approach is presented for inference. A major advantage of the approach is its simplicity and it can be easily implemented by using the existing software packages for right-censored failure time data. Extensive simulation studies are conducted and indicate that the approach performs well for practical situations and is comparable to the existing methods. The methodology is applied to a set of current status data arising from a tumorigenicity experiment and the model checking is discussed. [ABSTRACT FROM AUTHOR]

This article considers nonparametric comparison of survival functions, one of the most commonly required task in survival studies. For this, several test procedures have been proposed for interval-censored failure time data in which distributions of censoring intervals are identical among different treatment groups. Sometimes the distributions may depend on treatments and thus not be the same. A class of test statistics is proposed for situations where the distributions may be different for subjects in different treatment groups. The asymptotic normality of the test statistics is established and the test procedure is evaluated by simulations, which suggest that it works well for practical situations. An illustrative example is provided. [ABSTRACT FROM AUTHOR]

Panel count data frequently occur in many situations including medical follow-up studies and reliability experiments. For two-sample comparison based on panel count data, several procedures have been proposed including Thall and Lachin (1988) and Sun and Fang (2003). In this article, a new class of nonparametric test procedures are presented. The test is a generalization of that for the same problem for failure time data and overcomes some shortcomings of the existing methods. Monte Carlo simulation studies are conducted to evaluate the presented approach and suggest that it works well. An illustrative example is discussed. [ABSTRACT FROM AUTHOR]

Competing risks analysis of the case-cohort design is often required in epidemiological studies and for this, some methods have been proposed under the proportional hazards model. It is known that the proportional hazards model may not fit survival data well sometimes. This paper considers the analysis under the additive hazards model. Methods are proposed for estimating regression parameters and cumulative baseline hazard functions as well as cumulative incidence functions. The proposed estimators are shown to be consistent and asymptotically normal using the martingale central limit theory. Simulation studies are conducted and suggest that the proposed methods perform well. [ABSTRACT FROM AUTHOR]