Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction
AbstractLeft-truncated data are often encountered in epidemiological cohort studies, where individuals are recruited according to a certain cross-sectional sampling criterion. Length-biased data, a special case of left-truncated data, assume that the incidence of the initial event follows a homogeneous Poisson process. In this article, we consider an analysis of length-biased and interval-censored data with a nonsusceptible fraction. We first point out the importance of a well-defined target population, which depends on the prior knowledge for the support of the failure times of susceptible individuals. Given the target po...
Source: Lifetime Data Analysis - October 8, 2021 Category: Statistics Source Type: research
Continuous and discrete-time survival prediction with neural networks
AbstractDue to rapid developments in machine learning, and in particular neural networks, a number of new methods for time-to-event predictions have been developed in the last few years. As neural networks are parametric models, it is more straightforward to integrate parametric survival models in the neural network framework than the popular semi-parametric Cox model. In particular, discrete-time survival models, which are fully parametric, are interesting candidates to extend with neural networks. The likelihood for discrete-time survival data may be parameterized by the probability mass function (PMF) or by the discrete...
Source: Lifetime Data Analysis - October 7, 2021 Category: Statistics Source Type: research
Semiparametric analysis of multivariate panel count data with nonlinear interactions
AbstractMultivariate panel count data frequently arise in follow up studies involving several related types of recurrent events. For univariate panel count data, several varying coefficient models have been developed. However, varying coefficient models for multivariate panel count data remain to be studied. In this paper, we propose a varying coefficient mean model for multivariate panel count data to describe the possible nonlinear interact effects between the covariates and the local logarithm partial likelihood procedure is considered to estimate the unknown covariate effects. Furthermore, a Breslow-type estimator is c...
Source: Lifetime Data Analysis - October 5, 2021 Category: Statistics Source Type: research
A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models
AbstractMulti-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumption can often be unrealistic. The Markov assumption is seldomly checked and violations can lead to biased estimation of many parameters of interest. This is a well known problem for the standard Aalen-Johansen estimator of transition probabilities and several alternative estimators, not relying on the Markov assumption, have been suggested. A particularly simple approach known as landmarking have resulted in the Landmark-Aalen-Johansen est...
Source: Lifetime Data Analysis - September 30, 2021 Category: Statistics Source Type: research
A generalized theory of separable effects in competing event settings
AbstractIn competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However, effects of treatment on the competing event may indirectly contribute to this total effect, complicating its interpretation. We previously proposed theseparable effects to define direct and indirect effects of the treatment on the event of interest. This definition was given in a simple setting, where the treatment was decomposed into two components acting along two separate causal pathways. Here we generalize the notion of separable effe...
Source: Lifetime Data Analysis - September 1, 2021 Category: Statistics Source Type: research
Conditional screening for ultrahigh-dimensional survival data in case-cohort studies
AbstractThe case-cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies. In many such studies, the number of covariates is very large, and the goal of the research is to identify active covariates which have great influence on response. Since the introduction of sure independence screening, screening procedures have achieved great success in terms of effectively reducing the dimensionality and identifying active covariates. However, commonly used screening methods are based on marginal correlation or its variants, they may fail to identify hidden active variables which are j...
Source: Lifetime Data Analysis - August 20, 2021 Category: Statistics Source Type: research
Weighted Lindley frailty model: estimation and application to lung cancer data
AbstractIn this paper, we propose a novel frailty model for modeling unobserved heterogeneity present in survival data. Our model is derived by using a weighted Lindley distribution as the frailty distribution. The respective frailty distribution has a simple Laplace transform function which is useful to obtain marginal survival and hazard functions. We assume hazard functions of the Weibull and Gompertz distributions as the baseline hazard functions. A classical inference procedure based on the maximum likelihood method is presented. Extensive simulation studies are further performed to verify the behavior of maximum like...
Source: Lifetime Data Analysis - July 30, 2021 Category: Statistics Source Type: research
The MLE of the uniform distribution with right-censored data
AbstractWe carry out parametric inferences to a breast cancer data set which is right censored using the uniform distributionU(a, b). Under right censoring, it is rare that one can find the explicit solution to the maximum likelihood estimator (MLE) under the parametric set-up, except for the exponential distribution\(Exp(\theta )\). We show that the MLE ofa has a closed form solution, whereas the MLE ofb has a closed form solution in some sense. We further propose a diagnostic plotting method and test forU(a, b). The asymptotic properties of the MLE are also investigated. It turns out that this breast cancer data set ...
Source: Lifetime Data Analysis - July 25, 2021 Category: Statistics Source Type: research
Instrumental variable estimation of early treatment effect in randomized screening trials
AbstractThe primary analysis of randomized screening trials for cancer typically adheres to the intention-to-screen principle, measuring cancer-specific mortality reductions between screening and control arms. These mortality reductions result from a combination of the screening regimen, screening technology and the effect of the early, screening-induced, treatment. This motivates addressing these different aspects separately. Here we are interested in the causal effect of early versus delayed treatments on cancer mortality among the screening-detectable subgroup, which under certain assumptions is estimable from conventio...
Source: Lifetime Data Analysis - July 12, 2021 Category: Statistics Source Type: research
An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data
AbstractIn medical studies, the collected covariates contain underlying outliers. For clustered/longitudinal data with censored observations, the traditional Gehan-type estimator is robust to outliers in response but sensitive to outliers in the covariate domain, and it also ignores the within-cluster correlations. To take account of within-cluster correlations, varying cluster sizes, and outliers in covariates, we propose weighted Gehan-type estimating functions for parameter estimation in the accelerated failure time model for clustered data. We provide the asymptotic properties of the resulting estimators and carry out ...
Source: Lifetime Data Analysis - July 2, 2021 Category: Statistics Source Type: research
Factor copula models for right-censored clustered survival data
AbstractIn this article we extend the factor copula model to deal with right-censored event time data grouped in clusters. The new methodology allows for clusters to have variable sizes ranging from small to large and intracluster dependence to be flexibly modeled by any parametric family of bivariate copulas, thus encompassing a wide range of dependence structures. Incorporation of covariates (possibly time dependent) in the margins is also supported. Three estimation procedures are proposed: both one- and two-stage parametric and a two-stage semiparametric method where marginal survival functions are estimated by using a...
Source: Lifetime Data Analysis - June 14, 2021 Category: Statistics Source Type: research
Augmented likelihood for incorporating auxiliary information into left-truncated data
AbstractTime-to-event data are often subject to left-truncation. Lack of consideration of the sampling condition will introduce bias and loss in efficiency of the estimation. While auxiliary information from the same or similar cohorts may be available, challenges arise due to the practical issue of accessibility of individual-level data and taking account of various sampling conditions for different cohorts. In this paper, we introduce a likelihood-based method to incorporate information from auxiliary data to eliminate the left-truncation problem and improve efficiency. A one-step Monte-Carlo Expectation-Maximization alg...
Source: Lifetime Data Analysis - May 27, 2021 Category: Statistics Source Type: research
A varying-coefficient model for gap times between recurrent events
AbstractRecurrent events often arise in follow-up studies where a subject may experience multiple occurrences of the same type of event. Most regression models for recurrent events consider the time scale measured from the study origin and assume constant effects of covariates. In many applications, however, gap times between recurrent events are of natural interest and moreover the effects may actually vary over time. In this article, we propose a marginal varying-coefficient model for gap times between recurrent events that allows for the intra-individual correlation between events. Estimation and inference procedures ar...
Source: Lifetime Data Analysis - May 8, 2021 Category: Statistics Source Type: research
Regression analysis of current status data with latent variables
AbstractCurrent status data occur in many fields including demographical, epidemiological, financial, medical, and sociological studies. We consider the regression analysis of current status data with latent variables. The proposed model consists of a factor analytic model for characterizing latent variables through their multiple surrogates and an additive hazard model for examining potential covariate effects on the hazards of interest in the presence of current status data. We develop a borrow-strength estimation procedure that incorporates the expectation –maximization algorithm and correlated estimating equations. T...
Source: Lifetime Data Analysis - April 24, 2021 Category: Statistics Source Type: research
OptBand: optimization-based confidence bands for functions to characterize time-to-event distributions
AbstractClassical simultaneous confidence bands for survival functions (i.e., Hall –Wellner, equal precision, and empirical likelihood bands) are derived from transformations of the asymptotic Brownian nature of the Nelson–Aalen or Kaplan–Meier estimators. Due to the properties of Brownian motion, a theoretical derivation of the highest confidence density region cannot be ob tained in closed form. Instead, we provide confidence bands derived from a related optimization problem with local time processes. These bands can be applied to the one-sample problem regarding both cumulative hazard and survival functions. In ad...
Source: Lifetime Data Analysis - April 13, 2021 Category: Statistics Source Type: research
