Results 261 to 270 of about 1,623,179 (299)

Discrete‐time survival models with long‐term survivors

Statistics in Medicine, 2008
AbstractDiscrete‐time survival data typically possess three features: discreteness, ties, and concomitant information, which require appropriate discrete‐time models to analyze. In this paper, we first review some existing discrete‐time survival models and then extend them to discrete‐timecure survival models, which account for the presence oflong‐term
Xiaobing, Zhao, Xian, Zhou
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A Note on Standard Errors for Survival Curves in Discrete-Time Survival Analysis

Journal of Educational and Behavioral Statistics, 2005
Cox (1972) proposed a discrete-time survival model that is somewhat analogous to the proportional hazards model for continuous time. Efron (1988) showed that this model can be estimated using ordinary logistic regression software, and Singer and Willett (1993) provided a detailed illustration of a particularly flexible form of the model that includes ...
Rebecca Zwick, Jeffrey C. Sklar
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Modeling Clustered, Discrete, or Grouped Time Survival Data with Covariates

Biometrics, 1999
Summary.We have developed methods for modeling discrete or grouped time, right‐censored survival data collected from correlated groups or clusters. We assume that the marginal hazard of failure for individual items within a cluster is specified by a linear log odds survival model and the dependence structure is based on a gamma frailty model.
Ross, Eric A., Moore, Dirk
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A Discrete Survival Model with Random Effects: An Application to Time to Pregnancy

Biometrics, 1997
Time to pregnancy, the number of menstrual cycles it takes a couple to conceive, and various covariates have been collected among couples ultimately achieving conception. To assess the influence of the covariates, we constructed a discrete survival model that allows time-dependent covariates.
Scheike, Thomas H., Jensen, Tina Kold
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Cost-effective designs for trials with discrete-time survival endpoints

Computational Statistics & Data Analysis, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Katarzyna Józwiak, Mirjam Moerbeek
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Continuous and Discrete Time Survival Analysis: Neural Network Approaches

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007
In this paper we describe and compare two neural network models aimed at survival analysis modeling, based on formulations in continuous and discrete time. Learning in both models is approached in a Bayesian inference framework. We test the models on a real survival analysis problem, and we show that both models exhibit good discrimination and ...
A, Eleuteri, M S H, Aung, A F G, Taktak, B, Damato, P J G, Lisboa   +4 more
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Survival trees for discrete failure times

2013
GMDS 2013; 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS)
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The role of censoring on progression free survival: Oncologist discretion advised

European Journal of Cancer, 2015
Censoring is increasingly appreciated as a potential bias affecting estimates of progression free survival (PFS) in randomised trials. In this commentary, we explore the central assumption of censoring. Censored patients are considered no more or less likely to undergo the event of interest than those who remain in the analysis. Instead however, if one
Vinay, Prasad, Usama, Bilal
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Regression analysis of discrete time survival data under heterogeneity

Statistics in Medicine, 1997
This paper concerns the regression analysis of discrete time survival data for heterogeneous populations by means of frailty models. We express the survival time for each individual as a sequence of binary variables that indicate if the individual survived at each time point.
X, Xue, R, Brookmeyer
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Bayesian Analysis of Discrete Survival Data with a Hidden Markov Chain

Biometrics, 2000
Summary.This paper considers the discrete survival data from a Bayesian point of view. A sequence of the baseline hazard functions, which plays an important role in the discrete hazard function, is modeled with a hidden Markov chain. It is explained how the resultant model is implemented via Markov chain Monte Carlo methods. The model is illustrated by
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