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Lifetime Data Analysis

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https://www.readbyqxmd.com/read/28536818/bayesian-bivariate-survival-analysis-using-the-power-variance-function-copula
#1
Jose S Romeo, Renate Meyer, Diego I Gallardo
Copula 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...
May 23, 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/28349290/exponentiated-weibull-regression-for-time-to-event-data
#2
Shahedul A Khan
The Weibull, log-logistic and log-normal distributions are extensively used to model time-to-event data. The Weibull family accommodates only monotone hazard rates, whereas the log-logistic and log-normal are widely used to model unimodal hazard functions. The increasing availability of lifetime data with a wide range of characteristics motivate us to develop more flexible models that accommodate both monotone and nonmonotone hazard functions. One such model is the exponentiated Weibull distribution which not only accommodates monotone hazard functions but also allows for unimodal and bathtub shape hazard rates...
March 27, 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/26880366/landmark-estimation-of-survival-and-treatment-effects-in-observational-studies
#3
Layla Parast, Beth Ann Griffin
Clinical studies aimed at identifying effective treatments to reduce the risk of disease or death often require long term follow-up of participants in order to observe a sufficient number of events to precisely estimate the treatment effect. In such studies, observing the outcome of interest during follow-up may be difficult and high rates of censoring may be observed which often leads to reduced power when applying straightforward statistical methods developed for time-to-event data. Alternative methods have been proposed to take advantage of auxiliary information that may potentially improve efficiency when estimating marginal survival and improve power when testing for a treatment effect...
April 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/26423302/nonparametric-inference-for-the-joint-distribution-of-recurrent-marked-variables-and-recurrent-survival-time
#4
Laura M Yee, Kwun Chuen Gary Chan
Time between recurrent medical events may be correlated with the cost incurred at each event. As a result, it may be of interest to describe the relationship between recurrent events and recurrent medical costs by estimating a joint distribution. In this paper, we propose a nonparametric estimator for the joint distribution of recurrent events and recurrent medical costs in right-censored data. We also derive the asymptotic variance of our estimator, a test for equality of recurrent marker distributions, and present simulation studies to demonstrate the performance of our point and variance estimators...
April 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/28238045/estimation-of-the-cumulative-incidence-function-under-multiple-dependent-and-independent-censoring-mechanisms
#5
Judith J Lok, Shu Yang, Brian Sharkey, Michael D Hughes
Competing 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...
February 25, 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/28224260/modeling-restricted-mean-survival-time-under-general-censoring-mechanisms
#6
Xin Wang, Douglas E Schaubel
Restricted 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...
February 21, 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/28215038/variable-selection-and-prediction-in-biased-samples-with-censored-outcomes
#7
Ying Wu, Richard J Cook
With 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...
February 18, 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/28168333/conditional-maximum-likelihood-estimation-in-semiparametric-transformation-model-with-ltrc-data
#8
Chyong-Mei Chen, Pao-Sheng Shen
Left-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 the regression parameters and cumulative hazard function of these models...
February 6, 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/28132157/evaluation-of-the-treatment-time-lag-effect-for-survival-data
#9
Kayoung Park, Peihua Qiu
Medical 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 two treatments properly after the time-lag effect is accommodated...
January 28, 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/28091785/editorial
#10
EDITORIAL
Mei-Cheng Wang, Chiung-Yu Huang
No abstract text is available yet for this article.
January 16, 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/28058569/regression-analysis-of-current-status-data-with-auxiliary-covariates-and-informative-observation-times
#11
Yanqin Feng, Yurong Chen
This paper discusses regression analysis of current status failure time data with information observations and continuous auxiliary covariates. Under the additive hazards model, we employ a frailty model to describe the relationship between the failure time of interest and censoring time through some latent variables and propose an estimated partial likelihood estimator of regression parameters that makes use of the available auxiliary information. Asymptotic properties of the resulting estimators are established...
January 5, 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/27388910/acceleration-of-expectation-maximization-algorithm-for-length-biased-right-censored-data
#12
Kwun Chuen Gary Chan
Vardi's Expectation-Maximization (EM) algorithm is frequently used for computing the nonparametric maximum likelihood estimator of length-biased right-censored data, which does not admit a closed-form representation. The EM algorithm may converge slowly, particularly for heavily censored data. We studied two algorithms for accelerating the convergence of the EM algorithm, based on iterative convex minorant and Aitken's delta squared process. Numerical simulations demonstrate that the acceleration algorithms converge more rapidly than the EM algorithm in terms of number of iterations and actual timing...
January 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/27086362/nonparametric-and-semiparametric-regression-estimation-for-length-biased-survival-data
#13
Yu Shen, Jing Ning, Jing Qin
For the past several decades, nonparametric and semiparametric modeling for conventional right-censored survival data has been investigated intensively under a noninformative censoring mechanism. However, these methods may not be applicable for analyzing right-censored survival data that arise from prevalent cohorts when the failure times are subject to length-biased sampling. This review article is intended to provide a summary of some newly developed methods as well as established methods for analyzing length-biased data...
January 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/27007859/joint-modeling-of-longitudinal-and-survival-data-with-the-cox-model-and-two-phase-sampling
#14
Rong Fu, Peter B Gilbert
A common objective of cohort studies and clinical trials is to assess time-varying longitudinal continuous biomarkers as correlates of the instantaneous hazard of a study endpoint. We consider the setting where the biomarkers are measured in a designed sub-sample (i.e., case-cohort or two-phase sampling design), as is normative for prevention trials. We address this problem via joint models, with underlying biomarker trajectories characterized by a random effects model and their relationship with instantaneous risk characterized by a Cox model...
January 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/26759313/recent-progresses-in-outcome-dependent-sampling-with-failure-time-data
#15
Jieli Ding, Tsui-Shan Lu, Jianwen Cai, Haibo Zhou
An outcome-dependent sampling (ODS) design is a retrospective sampling scheme where one observes the primary exposure variables with a probability that depends on the observed value of the outcome variable. When the outcome of interest is failure time, the observed data are often censored. By allowing the selection of the supplemental samples depends on whether the event of interest happens or not and oversampling subjects from the most informative regions, ODS design for the time-to-event data can reduce the cost of the study and improve the efficiency...
January 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/27933468/conditional-screening-for-ultra-high-dimensional-covariates-with-survival-outcomes
#16
Hyokyoung G Hong, Jian Kang, Yi Li
Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization...
December 8, 2016: Lifetime Data Analysis
https://www.readbyqxmd.com/read/27900633/two-phase-outcome-dependent-studies-for-failure-times-and-testing-for-effects-of-expensive-covariates
#17
J F Lawless
Two- or multi-phase study designs are often used in settings involving failure times. In most studies, whether or not certain covariates are measured on an individual depends on their failure time and status. For example, when failures are rare, case-cohort or case-control designs are used to increase the number of failures relative to a random sample of the same size. Another scenario is where certain covariates are expensive to measure, so they are obtained only for selected individuals in a cohort. This paper considers such situations and focuses on cases where we wish to test hypotheses of no association between failure time and expensive covariates...
November 29, 2016: Lifetime Data Analysis
https://www.readbyqxmd.com/read/27761797/regression-analysis-of-clustered-failure-time-data-with-informative-cluster-size-under-the-additive-transformation-models
#18
Ling Chen, Yanqin Feng, Jianguo Sun
This paper discusses regression analysis of clustered failure time data, which occur when the failure times of interest are collected from clusters. In particular, we consider the situation where the correlated failure times of interest may be related to cluster sizes. For inference, we present two estimation procedures, the weighted estimating equation-based method and the within-cluster resampling-based method, when the correlated failure times of interest arise from a class of additive transformation models...
October 19, 2016: Lifetime Data Analysis
https://www.readbyqxmd.com/read/26511033/mark-specific-hazard-ratio-model-with-missing-multivariate-marks
#19
Michal Juraska, Peter B Gilbert
An objective of randomized placebo-controlled preventive HIV vaccine efficacy (VE) trials is to assess the relationship between vaccine effects to prevent HIV acquisition and continuous genetic distances of the exposing HIVs to multiple HIV strains represented in the vaccine. The set of genetic distances, only observed in failures, is collectively termed the 'mark.' The objective has motivated a recent study of a multivariate mark-specific hazard ratio model in the competing risks failure time analysis framework...
October 2016: Lifetime Data Analysis
https://www.readbyqxmd.com/read/26493471/cox-regression-with-missing-covariate-data-using-a-modified-partial-likelihood-method
#20
Torben Martinussen, Klaus K Holst, Thomas H Scheike
Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard function with the idea being to profile out this function before carrying out the estimation of the parameter of interest. In this step one uses a Breslow type estimator to estimate the cumulative baseline hazard function...
October 2016: Lifetime Data Analysis
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