Analysis Of Interval-censored Data Research Articles

Semiparametric varying-coefficient additive hazards model for informatively interval-censored failure time data

The analysis of interval-censored data with informative observation processes has recently attracted much attention and a few methods have been developed. However, all of the existing methods assume that covariate effects are constant and this may not be true sometimes in practice. To address this, in this paper, we propose a varying-coefficient additive hazards model and in the proposed method, frailty variables are used to describe the dependence between the failure time and the informative observation process. For inference, a Bernstein polynomials-based, two-step sieve maximum likelihood method is developed and the proposed method can be easily implemented. Furthermore, the resulting estimators of regression parameters are shown to be consistent and asymptotically normal. An extensive simulation study is conducted and suggests that the proposed approach works well for practical situations. The proposed method is applied to a cardiac allograft vasculopathy (CAV) study that motivated this investigation.

Efficient estimation for accelerated failure time model with interval-censored data in the presence of a cured subgroup

As the alternative of Cox model, the accelerated failure time (AFT) model, which simply regresses the logarithm of the survival time over the covariates, is commonly used in the analysis of interval-censored data. In this paper, we propose a novel two-component mixture-cure model for the interval-censored failure time data in the presence of a cure fraction. Specifically, the first component is a logistic regression model that describes the cure rate, and the second component is a semiparametric accelerated failure time model that describes the failure time of interest for the uncured subjects. An efficient semiparametric procedure is developed to estimate parameters in the considered model. We propose a penalized sieve maximum likelihood estimation approach with Bernstein polynomials to estimate the regression parameters quickly and accurately and the proposed procedure does not rely on the assumption of the distribution of the measurement error. The asymptotic properties of the resulting estimators are established. Extensive simulation studies conducted indicate that the proposed procedure works well for practical situations. In addition, AIDS data analysis is provided for illustration of the proposed method.

Canine anal sac gland carcinoma with regional lymph node metastases treated with sacculectomy and lymphadenectomy: Outcome and possible prognostic factors.

The staging system commonly used in canine anal sac gland carcinoma (ASGC) is a revised Tumour-Node-Metastasis (TNM) system published in 2007. This staging system consists in four stages and, for dogs with nodal metastases, the size of the metastatic lymph node (mLN) defines the N stage. However, we hypothesise that (1) the mLN size has no prognostic significance when the mLN can be excised, (2) a high number of mLNs is associated with poorer prognosis and (3) the measurement of the mLN on imaging is not reproducible. To investigate these hypotheses, medical records and diagnostic images of dogs with ASGC and mLN, treated with sacculectomy and lymphadenectomy, with or without chemotherapy, were reviewed. Interobserver variability for mLN measurement was assessed. Prognostic factors including mLN size and number were investigated. Time to documented progression (TDP) and disease-specific survival (DSS) were evaluated. Progression-free interval (PFI) was analysed with interval-censored data analysis. Fifty-seven dogs were included. The median PFI, TDP and DSS were 110 (95%CI 61.5-185.5), 196 (95%CI 162-283) and 340 days (95%CI 321-471), respectively. For measurement of the largest mLN, interobserver agreement was excellent but limits of agreement reached 39.7%. Neither the size of the largest mLN nor the use of adjuvant chemotherapy were associated with outcome. The number of mLNs was associated with outcome and having more than four mLNs was associated with shorter PFI (p< .001), TDP (p= .004) and DSS (p< .001). While mLN size measurement was not consistently reproducible and did not influence outcome in our cohort, number of mLNs did. Further studies are required for development of a revised staging system.

An ensemble method for interval-censored time-to-event data.

Interval-censored data analysis is important in biomedical statistics for any type of time-to-event response where the time of response is not known exactly, but rather only known to occur between two assessment times. Many clinical trials and longitudinal studies generate interval-censored data; one common example occurs in medical studies that entail periodic follow-up. In this article, we propose a survival forest method for interval-censored data based on the conditional inference framework. We describe how this framework can be adapted to the situation of interval-censored data. We show that the tuning parameters have a non-negligible effect on the survival forest performance and guidance is provided on how to tune the parameters in a data-dependent way to improve the overall performance of the method. Using Monte Carlo simulations, we find that the proposed survival forest is at least as effective as a survival tree method when the underlying model has a tree structure, performs similarly to an interval-censored Cox proportional hazards model fit when the true relationship is linear, and outperforms the survival tree method and Cox model when the true relationship is nonlinear. We illustrate the application of the method on a tooth emergence data set.

A flexible Bayesian non-parametric approach for fitting the odds to case II interval-censored data

Interval-censored survival data arise often in medical applications and clinical trials [Wang L, Sun J, Tong X. Regression analyis of case II interval-censored failure time data with the additive hazards model. Statistica Sinica. 2010;20:1709–1723]. However, most of existing interval-censored survival analysis techniques suffer from challenges such as heavy computational cost or non-proportionality of hazard rates due to complicated data structure [Wang L, Lin X. A Bayesian approach for analyzing case 2 interval-censored data under the semiparametric proportional odds model. Statistics & Probability Letters. 2011;81:876–883; Banerjee T, Chen M-H, Dey DK, et al. Bayesian analysis of generalized odds-rate hazards models for survival data. Lifetime Data Analysis. 2007;13:241–260]. To address these challenges, in this paper, we introduce a flexible Bayesian non-parametric procedure for the estimation of the odds under interval censoring, case II. We use Bernstein polynomials to introduce a prior for modeling the odds and propose a novel and easy-to-implement sampling manner based on the Markov chain Monte Carlo algorithms to study the posterior distributions. We also give general results on asymptotic properties of the posterior distributions. The simulated examples show that the proposed approach is quite satisfactory in the cases considered. The use of the proposed method is further illustrated by analyzing the hemophilia study data [McMahan CS, Wang L. A package for semiparametric regression analysis of interval-censored data; 2015. http://CRAN.R-project.org/package=ICsurv.

A flexible, computationally efficient method for fitting the proportional hazards model to interval-censored data.

The proportional hazards model (PH) is currently the most popular regression model for analyzing time-to-event data. Despite its popularity, the analysis of interval-censored data under the PH model can be challenging using many available techniques. This article presents a new method for analyzing interval-censored data under the PH model. The proposed approach uses a monotone spline representation to approximate the unknown nondecreasing cumulative baseline hazard function. Formulating the PH model in this fashion results in a finite number of parameters to estimate while maintaining substantial modeling flexibility. A novel expectation-maximization (EM) algorithm is developed for finding the maximum likelihood estimates of the parameters. The derivation of the EM algorithm relies on a two-stage data augmentation involving latent Poisson random variables. The resulting algorithm is easy to implement, robust to initialization, enjoys quick convergence, and provides closed-form variance estimates. The performance of the proposed regression methodology is evaluated through a simulation study, and is further illustrated using data from a large population-based randomized trial designed and sponsored by the United States National Cancer Institute.

Interval Censored Data Analysis with Weibull and Exponential Distribution

Interval-censored data result from incomplete observation of random variable when the values are known only to lie in an interval, instead of being observed exactly. The method of Maximum Likelihood Estimation is used to assess the parameters of the supposed distribution. Unlike in case of Weibull distribution, for regular test schedule of exponentially distributed data a closed formula is derived. Due to surprising simplicity the derived formula is open to direct interpretation in terms of observed input information.

Analysis of Interval-Censored Data with Random Unknown End Points: An Application to Soft Error Rate Estimation

Summary The paper presents a Bayesian approach to analysing interval-censored data with random unknown end points. Such data occur when the event of interest is interval censored but, because of the measurement process, the interval end points are not known exactly. Modelling the measurement process permits inference that accounts for this source of variability. Our results are motivated by an experimental study that was designed to characterize the cosmic-ray–neutron-induced soft error rate of a semiconductor device.

Study of an imputation algorithm for the analysis of interval-censored data

In this article, an iterative single-point imputation (SPI) algorithm, called quantile-filling algorithm for the analysis of interval-censored data, is studied. This approach combines the simplicity of the SPI and the iterative thoughts of multiple imputation. The virtual complete data are imputed by conditional quantiles on the intervals. The algorithm convergence is based on the convergence of the moment estimation from the virtual complete data. Simulation studies have been carried out and the results are shown for interval-censored data generated from the Weibull distribution. For the Weibull distribution, complete procedures of the algorithm are shown in closed forms. Furthermore, the algorithm is applicable to the parameter inference with other distributions. From simulation studies, it has been found that the algorithm is feasible and stable. The estimation accuracy is also satisfactory.

Analysis of Interval-Censored Data from Clustered Multistate Processes: Application to Joint Damage in Psoriatic Arthritis

Summary A conditionally Markov multiplicative intensity model is described for the analysis of clustered progressive multistate processes under intermittent observation. The model is motivated by a long-term prospective study of patients with psoriatic arthritis with the aim of characterizing progression of joint damage via an irreversible four-state model. The model accommodates heterogeneity in transition rates between different individuals and correlation in transition rates within patients. To do this we introduce subject-specific multivariate random effects in which each component acts multiplicatively on a specific transition intensity. Through the association between the components of the random effect, correlations in transition intensities are accommodated. A Monte Carlo EM algorithm is developed for estimation, which features closed form expressions for estimators at each M-step.

Racial Differences in Pain During 1 Year Among Women with Metastatic Breast Cancer: A Hazards Analysis of Interval-Censored Data

Racial Differences in Pain During 1 Year Among Women with Metastatic Breast Cancer: A Hazards Analysis of Interval-Censored Data

Analysis of Interval-Censored Data From Fractionated Experiments Using Covariance Adjustment

Censored data are commonly observed in industrial experiments such as for life testing and reliability improvement. Analyzing censored data from highly fractionated experiments presents a challenging problem to experimenters because many traditional methods become inadequate. Motivated by the data from a fluorescent-lamp experiment, we consider in this article analyzing censored data from highly fractionated experiments using covariance adjustment based on multivariate multiple regression models, which make use of the joint distribution of multivariate response variables. The Bayesian approach is taken for the main statistical inference. The posterior distribution of the parameters is obtained using the data augmentation algorithm. We illustrate the methodology with the fluorescent-lamp experiment data. With the real example and a simulation study, we show that covariance adjustment can lead to both dramatic variance reduction and possible bias reduction.

Analysis of Interval-Censored Data from Fractionated Experiments Using Covariance Adjustment

Censored data are commonly observed in industrial experiments such as for life testing and reliability improvement. Analyzing censored data from highly fractionated experiments presents a challenging problem to experimenters because many traditional methods become inadequate. Motivated by the data from a fluorescent-lamp experiment, we consider in this article analyzing censored data from highly fractionated experiments using covariance adjustment based on multivariate multiple regression models, which make use of the joint distribution of multivariate response variables. The Bayesian approach is taken for the main statistical inference. The posterior distribution of the parameters is obtained using the data augmentation algorithm. We illustrate the methodology with the fluorescent-lamp experiment data. With the real example and a simulation study, we show that covariance adjustment can lead to both dramatic variance reduction and possible bias reduction.