Results 11 to 20 of about 6,720,136 (196)

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.
europepmc   +6 more sources

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.
europepmc   +5 more sources

Multiply-robust estimator of cumulative incidence function difference for right-censored competing risks data [PDF]

BMC Medical Research Methodology
Background In causal inference, estimating the average treatment effects (ATE) for competing risk outcomes requires robust adjustment for confounding and specialized methods to handle competing events.
Yifei Tian, Ying Wu
doaj   +2 more sources

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, Paduthol Godan Sankaran   +1 more
doaj   +2 more sources

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
doaj   +2 more sources

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
Zhang, Mei-Jie, Zhang, Xu, Scheike, Thomas   +2 more
exaly   +3 more sources

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
exaly   +3 more sources

Summarizing differences in cumulative incidence functions [PDF]

Statistics in Medicine, 2008
AbstractThe cumulative incidence function is widely reported in competing risks studies, with group differences assessed by an extension of the log‐rank test. However, simple, interpretable summaries of group differences are not available. An adaptation of the proportional hazards model to the cumulative incidence function is often employed, but the ...
Mei-Jie, Zhang, Jason, Fine
openaire   +4 more sources

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.
europepmc   +5 more sources

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
exaly   +3 more sources