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Smooth semi-nonparametric (SNP) estimation of the cumulative incidence function. [PDF]
Stat Med, 2017 This paper presents a novel approach to estimation of the cumulative incidence function in the presence of competing risks. The underlying statistical model is specified via a mixture factorization of the joint distribution of the event type and the time to the event.
Duc AN, Wolbers M.
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Semiparametric regression on cumulative incidence function with interval-censored competing risks data. [PDF]
Stat Med, 2017 Many biomedical and clinical studies with time‐to‐event outcomes involve competing risks data. These data are frequently subject to interval censoring. This means that the failure time is not precisely observed but is only known to lie between two observation times such as clinical visits in a cohort study.
Bakoyannis G, Yu M, Yiannoutsos CT.
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Nonparametric Estimation of Cumulative Incidence Functions of Recurrent Events
Statistica, 2023 The present paper discusses modeling and analysis of recurrent event data with competing risks. We propose non parametric estimation of cumulative incidence functions of recurrent event competing risks model.
Sisuma Mandakathingal Sivadasan +1 more
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Cumulative Incidence Function in Studies on the Duration of the Unemployment Exit Process [PDF]
Folia Oeconomica Stetinensia, 2017 When we analyse the employment seeking process, an event that ends the observation of a given individual is their employment. The remaining observations are considered to be censored: the observations concluded before the end of the study or the cases of
Bieszk-Stolorz Beata
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Environment International, 2021 Background: In competing risks settings, the cause-specific cumulative incidence function is of great interest since it quantifies cumulative risk in the presence of other causes.
Daisuke Onozuka +3 more
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Modeling cumulative incidence function for competing risks data [PDF]
Expert Review of Clinical Pharmacology, 2008 A frequent occurrence in medical research is that a patient is subject to different causes of failure, where each cause is known as a competing risk. The cumulative incidence curve is a proper summary curve, showing the cumulative failure rates over time due to a particular cause. A common question in medical research is to assess the covariate effects
Mei-Jie Zhang, Thomas H Scheike
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Direct parametric inference for the cumulative incidence function
Journal of the Royal Statistical Society Series C: Applied Statistics, 2006 SummaryIn survival data that are collected from phase III clinical trials on breast cancer, a patient may experience more than one event, including recurrence of the original cancer, new primary cancer and death. Radiation oncologists are often interested in comparing patterns of local or regional recurrences alone as first events to identify a ...
Jong-Hyeon Jeong, Jason Fine
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Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms. [PDF]
Lifetime Data Anal, 2018 Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent.
Lok JJ, Yang S, Sharkey B, Hughes MD.
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Assessing cumulative incidence functions under the semiparametric additive risk model [PDF]
Statistics in Medicine, 2009 AbstractIn analyzing competing risks data, a quantity of considerable interest is the cumulative incidence function. Often, the effect of covariates on the cumulative incidence function is modeled via the proportional hazards model for the cause‐specific hazard function.
Yanqing Sun, Rajeshwari Sundaram
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Comparison of competing risks models based on cumulative incidence function in analyzing time to cardiovascular diseases [PDF]
ARYA Atherosclerosis, 2014 BACKGROUND: Competing risks arise when the subject is exposed to more than one cause of failure. Data consists of the time that the subject failed and an indicator of which risk caused the subject to fail.
Minoo Dianatkhah +6 more
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