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Semiparametric Models for Cumulative Incidence Functions
Biometrics, 2004Summary. In analyses of time‐to‐failure data with competing risks, cumulative incidence functions may be used to estimate the time‐dependent cumulative probability of failure due to specific causes. These functions are commonly estimated using nonparametric methods, but in cases where events due to the cause of primary interest are infrequent relative
Bryant, John, Dignam, James J.
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Comparing k Cumulative Incidence Functions Through Resampling Methods
Lifetime Data Analysis, 2002Tests for the equality of k cumulative incidence functions in a competing risks model are proposed. Test statistics are based on a vector of processes related to the cumulative incidence functions. Since their asymptotic distributions appear very complicated and depend on the underlying distribution of the data, two resampling techniques, namely the ...
Zhang, D, Zhu, L, Yuen, KC
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Prediction of Cumulative Incidence Function under the Proportional Hazards Model
Biometrics, 1998In the presence of dependent competing risks in survival analysis, the Cox model can be utilized to examine the covariate effects on the cause-specific hazard function for the failure type of interest. For this situation, the cumulative incidence function provides an intuitively appealing summary curve for marginal probabilities of this particular ...
Cheng, S. C., Fine, Jason P., Wei, L. J.
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Flexible parametric modelling of the cause‐specific cumulative incidence function
Statistics in Medicine, 2016Competing risks arise with time‐to‐event data when individuals are at risk of more than one type of event and the occurrence of one event precludes the occurrence of all other events. A useful measure with competing risks is the cause‐specific cumulative incidence function (CIF), which gives the probability of experiencing a particular event as a ...
Lambert, Paul C. +2 more
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Dynamic prediction of cumulative incidence functions by direct binomial regression
Biometrical Journal, 2018AbstractIn recent years there have been a series of advances in the field of dynamic prediction. Among those is the development of methods for dynamic prediction of the cumulative incidence function in a competing risk setting. These models enable the predictions to be updated as time progresses and more information becomes available, for example when ...
Grand, Mia K. +3 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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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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Restricted estimation of the cumulative incidence functions of two competing risks
Journal of Statistical Planning and Inference, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Al-Kandari, Noriah, El Barmi, Hammou
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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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Comparing cumulative incidence functions of a competing-risks model
IEEE Transactions on Reliability, 1997A competing-risks model refers to a situation where a system (or organism) is exposed to two or more causes of failure (or death) but its eventual failure (or death) can be attributed to exactly one of the causes of failure. The basic information available in the competing-risks situation is the time to failure of the system, and the corresponding ...
null Yanqing Sun, R.C. Tiwari
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