Optimal subsampling for semi‐parametric accelerated failure time models with massive survival data using a rank‐based approach
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DOI: 10.1002/sim.10200
Abstract: Subsampling is a practical strategy for analyzing vast survival data, which are progressively encountered across diverse research domains. While the optimal subsampling method has been applied to inferences for Cox models and parametric accelerated failure… read more here.
Keywords: parametric accelerated; failure time; time; survival data ... See more keywords
Multilevel model with random effects for clustered survival data with multiple failure outcomes.
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DOI: 10.1002/sim.8041
Abstract: We present a multilevel frailty model for handling serial dependence and simultaneous heterogeneity in survival data with a multilevel structure attributed to clustering of subjects and the presence of multiple failure outcomes. One commonly observes… read more here.
Keywords: multiple failure; methodology; survival data; random effects ... See more keywords
Frailty proportional mean residual life regression for clustered survival data: A hierarchical quasi-likelihood method.
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DOI: 10.1002/sim.8338
Abstract: Frailty models are widely used to model clustered survival data arising in multicenter clinical studies. In the literature, most existing frailty models are proportional hazards, additive hazards, or accelerated failure time model based. In this… read more here.
Keywords: likelihood; survival data; clustered survival; frailty ... See more keywords
Deep learning for the dynamic prediction of multivariate longitudinal and survival data
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DOI: 10.1002/sim.9392
Abstract: The joint model for longitudinal and survival data improves time‐to‐event predictions by including longitudinal outcome variables in addition to baseline covariates. However, in practice, joint models may be limited by parametric assumptions in both the… read more here.
Keywords: survival data; time event; longitudinal survival; survival ... See more keywords
Generalized mean residual life models for survival data with missing censoring indicators
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DOI: 10.1002/sim.9615
Abstract: The mean residual life (MRL) function is an important and attractive alternative to the hazard function for characterizing the distribution of a time‐to‐event variable. In this article, we study the modeling and inference of a… read more here.
Keywords: survival data; censoring indicators; generalized mean; mean residual ... See more keywords
New C‐indices for assessing importance of longitudinal biomarkers in fitting competing risks survival data in the presence of partially masked causes
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DOI: 10.1002/sim.9671
Abstract: Competing risks survival data in the presence of partially masked causes are frequently encountered in medical research or clinical trials. When longitudinal biomarkers are also available, it is of great clinical importance to examine associations… read more here.
Keywords: survival data; masked causes; data presence; risks survival ... See more keywords
Nonparametric estimation of marked survival data in the presence of dependent censoring
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DOI: 10.1002/sim.9710
Abstract: We consider nonparametrically estimating the joint distribution of a survival time and mark variable, where the survival time is subject to right censoring and the mark variable is only observed when the survival time is… read more here.
Keywords: estimation marked; survival data; marked survival; dependent censoring ... See more keywords
Feature screening for ultrahigh-dimensional survival data when failure indicators are missing at random
Sign Up to like & getrecommendations! Published in 2019 at "Statistical Papers"
DOI: 10.1007/s00362-019-01128-5
Abstract: In modern statistical applications, the dimension of covariates can be much larger than the sample size, and extensive research has been done on screening methods which can effectively reduce the dimensionality. However, the existing feature… read more here.
Keywords: missing random; feature screening; failure indicators; indicators missing ... See more keywords
Subsampling approach for least squares fitting of semi-parametric accelerated failure time models to massive survival data
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DOI: 10.1007/s11222-024-10391-y
Abstract: Massive survival data are increasingly common in many research fields, and subsampling is a practical strategy for analyzing such data. Although optimal subsampling strategies have been developed for Cox models, little has been done for… read more here.
Keywords: failure time; time; massive survival; survival data ... See more keywords
Nonparametric identification and estimation of dynamic treatment effects for survival data in a regression discontinuity design
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DOI: 10.1016/j.econlet.2019.108665
Abstract: Treatment assignment in the survival literature is often assumed to be allocated simultaneously and independently of prospective treatment gains. This paper relaxes these restrictions by introducing dynamic treatment assignment for survival data in a regression… read more here.
Keywords: dynamic treatment; treatment; data regression; survival data ... See more keywords
More powerful logrank permutation tests for two-sample survival data
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DOI: 10.1080/00949655.2020.1773463
Abstract: Weighted logrank tests are a popular tool for analysing right-censored survival data from two independent samples. Each of these tests is optimal against a certain hazard alternative, for example, the classical logrank test for proportional… read more here.
Keywords: logrank permutation; powerful logrank; permutation; tests two ... See more keywords