Lifetime Data Analysis最新文献

A probability method to estimate cancer risk for asymptomatic individuals for the rest of life was developed based on one's current age and screening history using the disease progressive model. The risk is a function of the transition probability density from the disease-free to the preclinical state, the sojourn time in the preclinical state and the screening sensitivity if one had a screening history with negative results. The method can be applied to any chronic disease. As an example, the method was applied to estimate women's breast cancer risk using parameters estimated from the Health Insurance Plan of Greater New York under two scenarios: with and without a screening history, and obtain some meaningful results.

In this study, we focus on estimating the heterogeneous treatment effect (HTE) for survival outcome. The outcome is subject to censoring and the number of covariates is high-dimensional. We utilize data from both the randomized controlled trial (RCT), considered as the gold standard, and real-world data (RWD), possibly affected by hidden confounding factors. To achieve a more efficient HTE estimate, such integrative analysis requires great insight into the data generation mechanism, particularly the accurate characterization of unmeasured confounding effects/bias. With this aim, we propose a penalized-regression-based integrative approach that allows for the simultaneous estimation of parameters, selection of variables, and identification of the existence of unmeasured confounding effects. The consistency, asymptotic normality, and efficiency gains are rigorously established for the proposed estimate. Finally, we apply the proposed method to estimate the HTE of lobar/sublobar resection on the survival of lung cancer patients. The RCT is a multicenter non-inferiority randomized phase 3 trial, and the RWD comes from a clinical oncology cancer registry in the United States. The analysis reveals that the unmeasured confounding exists and the integrative approach does enhance the efficiency for the HTE estimation.

In survival analysis, researchers commonly focus on variable selection issues in real-world data, particularly when complex network structures exist among covariates. Additionally, due to factors such as data collection costs and delayed entry, real-world data often exhibit censoring and truncation phenomena.This paper addresses left-truncated current status data by employing a copula-based approach to model the relationship between censoring time and failure time. Based on this, we investigate the problem of variable selection in the context of complex network structures among covariates. To this end, we integrate Markov Random Field (MRF) with the Proportional Hazards (PH) model, and extend the latter to more flexibly characterize the correlation structure among covariates. For solving the constructed model, we propose a penalized optimization method and utilize spline functions to estimate the baseline hazard function. Through numerical simulation experiments and case studies of clinical trial data, we comprehensively evaluate the effectiveness and performance of the proposed model and its parameter inference strategy. This evaluation not only demonstrates the robustness of the proposed model in handling complex disease data but also further verifies the high precision and reliability of the parameter estimation method.

To investigate pairwise interactions arising from recurrent event processes in a longitudinal network, the framework of the stochastic block model is followed, where every node belongs to a latent group and interactions between node pairs from two specified groups follow a conditional nonhomogeneous Poisson process. Our focus lies on discrete observation times, which are commonly encountered in reality for cost-saving purposes. The variational EM algorithm and variational maximum likelihood estimation are applied for statistical inference. A specific method based on the defined distribution function F and self-consistency algorithm for recurrent events is used when estimating the intensity functions of edges. Numerical simulations illustrate the performance of our proposed estimation procedure in uncovering the underlying structure in the longitudinal networks with recurrent event processes. The dataset of interactions between French schoolchildren for influenza monitoring is analyzed.

We propose a joint model for multiple time-to-event outcomes where the outcomes have a cure structure. When a subset of a population is not susceptible to an event of interest, traditional survival models cannot accommodate this type of phenomenon. For example, for patients with melanoma, certain modern treatment options can reduce the mortality and relapse rates. Traditional survival models assume the entire population is at risk for the event of interest, i.e., has a non-zero hazard at all times. However, cure rate models allow a portion of the population to be risk-free of the event of interest. Our proposed model uses a novel truncated Gaussian copula to jointly model bivariate time-to-event outcomes of this type. In oncology studies, multiple time-to-event outcomes (e.g., overall survival and relapse-free or progression-free survival) are typically of interest. Therefore, multivariate methods to analyze time-to-event outcomes with a cure structure are potentially of great utility. We formulate a joint model directly on the time-to-event outcomes (i.e., unconditional on whether an individual is cured or not). Dependency between the time-to-event outcomes is modeled via the correlation matrix of the truncated Gaussian copula. A Markov Chain Monte Carlo procedure is proposed for model fitting. Simulation studies and a real data analysis using a melanoma clinical trial data are presented to illustrate the performance of the method and the proposed model is compared to independent models.

The wild bootstrap is a popular resampling method in the context of time-to-event data analysis. Previous works established the large sample properties of it for applications to different estimators and test statistics. It can be used to justify the accuracy of inference procedures such as hypothesis tests or time-simultaneous confidence bands. This paper provides a general framework for establishing large sample properties in a unified way by using martingale structures. This framework includes most of the well-known parametric, semiparametric and nonparametric statistical methods in time-to-event analysis. Along the way of proving the validity of the wild bootstrap, a new variant of Rebolledo's martingale central limit theorem for counting process-based martingales is developed as well.

In this study, we investigate estimation and variable selection for semiparametric transformation models with length-biased survival data-a special case of left truncation commonly encountered in the social sciences and cancer prevention trials. To correct for sampling bias, conventional methods such as conditional likelihood, martingale estimating equations, and composite likelihood have been proposed. However, these methods may be less efficient due to their reliance on only partial information from the full likelihood. In contrast, we adopt a full-likelihood approach under the semiparametric transformation model and propose a unified and more efficient nonparametric maximum likelihood estimator (NPMLE). To perform variable selection, we incorporate an adaptive least absolute shrinkage and selection operator (ALASSO) penalty into the full likelihood. We show that when the NPMLE is used as the initial value, the resulting one-step ALASSO estimator-offering a simplified version of the Newton-Raphson method-achieves oracle properties. Theoretical properties of the proposed methods are established using empirical process techniques. The performance of the methods is evaluated through simulation studies and illustrated with a real data application.

In a typical individually randomized group-treatment (IRGT) trial, subjects are randomized between a control arm and an experimental arm. While the subjects randomized to the control arm are treated individually, those in the experimental arm are assigned to one of clusters for group treatment. By sharing some common frailties, the outcomes of subjects in the same groups tend to be dependent, whereas those in the control arm are independent. In this paper, we consider IRGT trials with time to event outcomes. We modify the two-sample log-rank test to compare the survival data from TRGT trials, and derive its sample size formula. The proposed sample size formula requires specification of marginal survival distributions for the two arms, bivariate survival distribution and cluster size distribution for the experimental arm, and accrual period or accrual rate together with additional follow-up period. In a sample size calculation, either the cluster sizes are given and the number of clusters is calculated or the number of clusters is given at the time of study open and the required accrual period to determine the cluster sizes is calculated. Simulations and a real data example show that the proposed test statistic controls the type I error rate and the formula provides accurately powered sample sizes. Also proposed are optimal designs minimizing the total sample size or the total cost when the cost per subject is different between two treatment arms.

The study of survival data often requires taking proper care of the censoring mechanism that prohibits complete observation of the data. Under right censoring, only the first occurring event is observed: either the event of interest, or a competing event like withdrawal of a subject from the study. The corresponding identifiability difficulties led many authors to imposing (conditional) independence or a fully known dependence between survival and censoring times, both of which are not always realistic. However, recent results in survival literature showed that parametric copula models allow identification of all model parameters, including the association parameter, under appropriately chosen marginal distributions. The present paper is the first one to apply such models in a quantile regression context, hence benefiting from its well-known advantages in terms of e.g. robustness and richer inference results. The parametric copula is supplemented with a likewise parametric, yet flexible, enriched asymmetric Laplace distribution for the survival times conditional on the covariates. Its asymmetric Laplace basis provides its close connection to quantiles, while the extension with Laguerre orthogonal polynomials ensures sufficient flexibility for increasing polynomial degrees. The distributional flavour of the quantile regression presented, comes with advantages of both theoretical and computational nature. All model parameters are proven to be identifiable, consistent, and asymptotically normal. Finally, performance of the model and of the proposed estimation procedure is assessed through extensive simulation studies as well as an application on liver transplant data.

In the context of time-to-event analysis, First hitting time methods consider the event occurrence as the ending point of some evolving process. The characteristics of the process are of great relevance for the analysis, which makes this class of models interesting and particularly suitable for applications where something about the degradation path is known. In cases where the degradation can only worsen, a monotonic process is the most suitable choice. This paper proposes a boosting algorithm for first hitting time models based on an underlying homogeneous gamma process to account for the monotonicity of the degradation trend. The predictive power and versatility of the algorithm are shown with real data examples from both engineering and biomedical applications, as well as with simulated examples.