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Constrained parametric model for simultaneous inference of two cumulative incidence functions
Biometrical Journal, 2012We propose a parametric regression model for the cumulative incidence functions (CIFs) commonly used for competing risks data. The model adopts a modified logistic model as the baseline CIF and a generalized odds‐rate model for covariate effects, and it explicitly takes into account the constraint that a subject with any given prognostic factors should
Shi, Haiwen +2 more
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Analyses of cumulative incidence functions via non‐parametric multiple imputation
Statistics in Medicine, 2008AbstractWe describe a non‐parametric multiple imputation method that recovers the missing potential censoring information from competing risks failure times for the analysis of cumulative incidence functions. The method can be applied in the settings of stratified analyses, time‐varying covariates, weighted analysis of case‐cohort samples and clustered
Ping K, Ruan, Robert J, Gray
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Biometrics, 2005
SummaryTypically, regression models for competing risks outcomes are based on proportional hazards models for the crude hazard rates. These estimates often do not agree with impressions drawn from plots of cumulative incidence functions for each level of a risk factor. We present a technique which models the cumulative incidence functions directly. The
Klein, John P., Andersen, Per Kragh
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SummaryTypically, regression models for competing risks outcomes are based on proportional hazards models for the crude hazard rates. These estimates often do not agree with impressions drawn from plots of cumulative incidence functions for each level of a risk factor. We present a technique which models the cumulative incidence functions directly. The
Klein, John P., Andersen, Per Kragh
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Empirical Likelihood Based Test for Equality of Cumulative Incidence Functions
Journal of the Indian Society for Probability and Statistics, 2020An empirical likelihood based test for comparing the incidence functions for multiple competing risks is proposed, without making any assumptions on the distribution of the failure times. The performance of the proposed method is assessed based on large number of simulations and compared with existing method.
Asokan Mulayath Variyath, P. G. Sankaran
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Confidence intervals for the cumulative incidence function via constrained NPMLE
Lifetime Data Analysis, 2018The cumulative incidence function (CIF) displays key information in the competing risks setting, which is common in medical research. In this article, we introduce two new methods to compute non-parametric confidence intervals for the CIF. First, we introduce non-parametric profile-likelihood confidence intervals.
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Maximum likelihood estimator for cumulative incidence functions under proportionality constraint
Sankhya A, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Geffray, Ségolen, Guilloux, Agathe
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On comparing competing risks using the ratio of their cumulative incidence functions
Annals of the Institute of Statistical Mathematics, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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NON-PARAMETRIC INFERENCE FOR CUMULATIVE INCIDENCE FUNCTIONS IN COMPETING RISKS STUDIES
Statistics in Medicine, 1997In the competing risks problem, a useful quantity is the cumulative incidence function, which is the probability of occurrence by time t for a particular type of failure in the presence of other risks. The estimator of this function as given by Kalbfleisch and Prentice is consistent, and, properly normalized, converges weakly to a zero-mean Gaussian ...
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Competing risks : modelling crude cumulative incidence functions
2005The clinical course of a disease is often characterized by the possible occurrence of several types of events, each one having a specific role for the evaluation of the therapeutical strategies. The event occurring as first is of particular interest, since it could be considered as ’’treatment failure’’ or ’’response to treatment’’.
P. Boracchi +3 more
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Lifetime Data Analysis, 2011
In the competing risks problem an important role is played by the cumulative incidence function (CIF), whose value at time t is the probability of failure by time t from a particular type of risk in the presence of other risks. Assume that the lifetime distributions of two populations are uniformly stochastically ordered.
Al-Kandari, Noriah M. A. +2 more
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In the competing risks problem an important role is played by the cumulative incidence function (CIF), whose value at time t is the probability of failure by time t from a particular type of risk in the presence of other risks. Assume that the lifetime distributions of two populations are uniformly stochastically ordered.
Al-Kandari, Noriah M. A. +2 more
openaire +3 more sources