Bayesian Expectile Joint Model With Varying Coefficient for Longitudinal and Semi‐Competing Risks Data
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DOI: 10.1002/sim.70219
Abstract: In the realm of clinical medical research, semi‐competing risks data are usually observed in practice, yet there are few studies on the joint models of longitudinal and semi‐competing risks data. In this paper, a joint… read more here.
Keywords: joint model; competing risks; risks data; model ... See more keywords
Marginal semiparametric transformation models for clustered multivariate competing risks data
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DOI: 10.1002/sim.9573
Abstract: Multivariate survival models are often used in studying multiple outcomes for right‐censored data. However, the outcomes of interest often have competing risks, where standard multivariate survival models may lead to invalid inferences. For example, patients… read more here.
Keywords: marginal semiparametric; risks data; multivariate competing; competing risks ... See more keywords
Modelling and analysis of recall-based competing risks data
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DOI: 10.1080/02664763.2018.1561833
Abstract: ABSTRACT In this paper we consider the analysis of recall-based competing risks data. The chance of an individual recalling the exact time to event depends on the time of occurrence of the event and time… read more here.
Keywords: risks data; analysis recall; recall; competing risks ... See more keywords
An additive subdistribution hazard model for competing risks data
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DOI: 10.1080/03610926.2016.1277759
Abstract: ABSTRACT The cumulative incidence function plays an important role in assessing its treatment and covariate effects with competing risks data. In this article, we consider an additive hazard model allowing the time-varying covariate effects for… read more here.
Keywords: subdistribution; hazard model; risks data; competing risks ... See more keywords
Regression modeling of cumulative incidence function for left-truncated right-censored competing risks data: A modified pseudo-observation approach
Sign Up to like & getrecommendations! Published in 2025 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2025.2458183
Abstract: Abstract Statistical methods have been developed for regression modeling of the cumulative incidence function (CIF) given left-truncated right-censored competing risks data. Nevertheless, existing methods typically involve complicated weighted estimating equations or non parametric conditional likelihood… read more here.
Keywords: right censored; regression modeling; left truncated; competing risks ... See more keywords
Scalable Algorithms for Large Competing Risks Data
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DOI: 10.1080/10618600.2020.1841650
Abstract: Abstract This article develops two orthogonal contributions to scalable sparse regression for competing risks time-to-event data. First, we study and accelerate the broken adaptive ridge method (BAR), a surrogate -based iteratively reweighted -penalization algorithm that… read more here.
Keywords: risks data; algorithm; competing risks; psh model ... See more keywords
Joint inference for competing risks data using multiple endpoints.
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DOI: 10.1111/biom.13752
Abstract: Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In… read more here.
Keywords: joint inference; data using; risks data; inference competing ... See more keywords
Doubly-robust estimator of the difference in restricted mean times lost with competing risks data
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DOI: 10.1177/09622802221102625
Abstract: In the context of competing risks data, the subdistribution hazard ratio has limited clinical interpretability to measure treatment effects. An alternative is the difference in restricted mean times lost (RMTL), which gives the mean time… read more here.
Keywords: risks data; difference restricted; treatment; competing risks ... See more keywords
Competing risks data in clinical oncology
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DOI: 10.3389/fonc.2024.1360266
Abstract: Competing risks data analysis plays a critical role in the evaluation of clinical utility of specific cancer treatments and can inform the development of future treatment approaches. Although competing risks data are ubiquitous in cancer… read more here.
Keywords: oncology; competing risks; risks data; oncology competing ... See more keywords