Alternating event processes during lifetimes: population dynamics and statistical inference
AbstractIn the literature studying recurrent event data, a large amount of work has been focused on univariate recurrent event processes where the occurrence of each event is treated as a single point in time. There are many applications, however, in which univariate recurrent events are insufficient to characterize the feature of the process because patients experience nontrivial durations associated with each event. This results in an alternating event process where the disease status of a patient alternates between exacerbations and remissions. In this paper, we consider the dynamics of a chronic disease and its associa...
Source: Lifetime Data Analysis - August 7, 2017 Category: Statistics Source Type: research
The competing risks Cox model with auxiliary case covariates under weaker missing-at-random cause of failure
AbstractIn the analysis of time-to-event data with multiple causes using a competing risks Cox model, often the cause of failure is unknown for some of the cases. The probability of a missing cause is typically assumed to be independent of the cause given the time of the event and covariates measured before the event occurred. In practice, however, the underlying missing-at-random assumption does not necessarily hold. Motivated by colorectal cancer molecular pathological epidemiology analysis, we develop a method to conduct valid analysis when additional auxiliary variables are available for cases only. We consider a weake...
Source: Lifetime Data Analysis - August 4, 2017 Category: Statistics Source Type: research
Group and within-group variable selection for competing risks data
AbstractVariable selection in the presence of grouped variables is troublesome for competing risks data: while some recent methods deal with group selection only, simultaneous selection of both groups and within-group variables remains largely unexplored. In this context, we propose an adaptive group bridge method, enabling simultaneous selection both within and between groups, for competing risks data. The adaptive group bridge is applicable to independent and clustered data. It also allows the number of variables to diverge as the sample size increases. We show that our new method possesses excellent asymptotic propertie...
Source: Lifetime Data Analysis - August 4, 2017 Category: Statistics Source Type: research
Reweighted estimators for additive hazard model with censoring indicators missing at random
AbstractSurvival data with missing censoring indicators are frequently encountered in biomedical studies. In this paper, we consider statistical inference for this type of data under the additive hazard model. Reweighting methods based on simple and augmented inverse probability are proposed. The asymptotic properties of the proposed estimators are established. Furthermore, we provide a numerical technique for checking adequacy of the fitted model with missing censoring indicators. Our simulation results show that the proposed estimators outperform the simple and augmented inverse probability weighted estimators without re...
Source: Lifetime Data Analysis - August 1, 2017 Category: Statistics Source Type: research
A regularized variable selection procedure in additive hazards model with stratified case-cohort design
AbstractCase-cohort designs are commonly used in large epidemiological studies to reduce the cost associated with covariate measurement. In many such studies the number of covariates is very large. An efficient variable selection method is needed for case-cohort studies where the covariates are only observed in a subset of the sample. Current literature on this topic has been focused on the proportional hazards model. However, in many studies the additive hazards model is preferred over the proportional hazards model either because the proportional hazards assumption is violated or the additive hazards model provides more ...
Source: Lifetime Data Analysis - July 28, 2017 Category: Statistics Source Type: research
Modeling of semi-competing risks by means of first passage times of a stochastic process
We present a model where the time to the terminal event is the first passage time to a fixed levelc in a stochastic process, while the time to the non-terminal event is represented by the first passage time of the same process to a stochastic thresholdS, assumed to be independent of the stochastic process. In order to be explicit, we let the stochastic process be a gamma process, but other processes with independent increments may alternatively be used. For semi-competing risks this appears to be a new modeling approach, being an alternative to traditional approaches based on illness-death models and copula models. In this...
Source: Lifetime Data Analysis - July 22, 2017 Category: Statistics Source Type: research
Joint analysis of interval-censored failure time data and panel count data
AbstractInterval-censored failure time data and panel count data are two types of incomplete data that commonly occur in event history studies and many methods have been developed for their analysis separately (Sun in The statistical analysis of interval-censored failure time data. Springer, New York,2006; Sun and Zhao in The statistical analysis of panel count data. Springer, New York,2013). Sometimes one may be interested in or need to conduct their joint analysis such as in the clinical trials with composite endpoints, for which it does not seem to exist an established approach in the literature. In this paper, a sieve ...
Source: Lifetime Data Analysis - June 12, 2017 Category: Statistics Source Type: research
Censored cumulative residual independent screening for ultrahigh-dimensional survival data
AbstractFor complete ultrahigh-dimensional data, sure independent screening methods can effectively reduce the dimensionality while retaining all the active variables with high probability. However, limited screening methods have been developed for ultrahigh-dimensional survival data subject to censoring. We propose a censored cumulative residual independent screening method that is model-free and enjoys the sure independent screening property. Active variables tend to be ranked above the inactive ones in terms of their association with the survival times. Compared with several existing methods, our model-free screening me...
Source: Lifetime Data Analysis - May 26, 2017 Category: Statistics Source Type: research
Bayesian bivariate survival analysis using the power variance function copula
AbstractCopula models have become increasingly popular for modelling the dependence structure in multivariate survival data. The two-parameter Archimedean family of Power Variance Function (PVF) copulas includes the Clayton, Positive Stable (Gumbel) and Inverse Gaussian copulas as special or limiting cases, thus offers a unified approach to fitting these important copulas. Two-stage frequentist procedures for estimating the marginal distributions and the PVF copula have been suggested by Andersen (Lifetime Data Anal 11:333 –350,2005), Massonnet et al. (J Stat Plann Inference 139(11):3865 –3877,2009) and Prenen et al. (...
Source: Lifetime Data Analysis - May 23, 2017 Category: Statistics Source Type: research
Exponentiated Weibull regression for time-to-event data
In this study, we show that the exponentiated Weibull distribution is closed under the accelerated failure time family. We then formulate a regression model based on the exponentiated Weibull distribution, and develop large sample theory for statistical inference. We also describe a Bayesian approach for inference. Two comparative studies based on real and simulated data sets reveal that the exponentiated Weibull regression can be valuable in adequately describing different types of time-to-event data. (Source: Lifetime Data Analysis)
Source: Lifetime Data Analysis - March 27, 2017 Category: Statistics Source Type: research
Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms
AbstractCompeting risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the eff...
Source: Lifetime Data Analysis - February 24, 2017 Category: Statistics Source Type: research
Modeling restricted mean survival time under general censoring mechanisms
AbstractRestricted mean survival time (RMST) is often of great clinical interest in practice. Several existing methods involve explicitly projecting out patient-specific survival curves using parameters estimated through Cox regression. However, it would often be preferable to directly model the restricted mean for convenience and to yield more directly interpretable covariate effects. We propose generalized estimating equation methods to model RMST as a function of baseline covariates. The proposed methods avoid potentially problematic distributional assumptions pertaining to restricted survival time. Unlike existing meth...
Source: Lifetime Data Analysis - February 20, 2017 Category: Statistics Source Type: research
Variable selection and prediction in biased samples with censored outcomes
AbstractWith the increasing availability of large prospective disease registries, scientists studying the course of chronic conditions often have access to multiple data sources, with each source generated based on its own entry conditions. The different entry conditions of the various registries may be explicitly based on the response process of interest, in which case the statistical analysis must recognize the unique truncation schemes. Moreover, intermittent assessment of individuals in the registries can lead to interval-censored times of interest. We consider the problem of selecting important prognostic biomarkers f...
Source: Lifetime Data Analysis - February 17, 2017 Category: Statistics Source Type: research
Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data
AbstractLeft-truncated data often arise in epidemiology and individual follow-up studies due to a biased sampling plan since subjects with shorter survival times tend to be excluded from the sample. Moreover, the survival time of recruited subjects are often subject to right censoring. In this article, a general class of semiparametric transformation models that include proportional hazards model and proportional odds model as special cases is studied for the analysis of left-truncated and right-censored data. We propose a conditional likelihood approach and develop the conditional maximum likelihood estimators (cMLE) for ...
Source: Lifetime Data Analysis - February 5, 2017 Category: Statistics Source Type: research
Evaluation of the treatment time-lag effect for survival data
AbstractMedical treatments often take a period of time to reveal their impact on subjects, which is the so-called time-lag effect in the literature. In the survival data analysis literature, most existing methods compare two treatments in the entire study period. In cases when there is a substantial time-lag effect, these methods would not be effective in detecting the difference between the two treatments, because the similarity between the treatments during the time-lag period would diminish their effectiveness. In this paper, we develop a novel modeling approach for estimating the time-lag period and for comparing the t...
Source: Lifetime Data Analysis - January 27, 2017 Category: Statistics Source Type: research
