English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recurrent events when we deal with survival studies demand a different methodology from what is used in standard survival analysis. The main problem that we found when we make inference in these kind of studies is that the observations may not be independent. Thus, biased and inefficient estimators can be obtained if we do not take into account this fact. In the independent case, the interocurrence survival function can be estimated by the generalization of the limit product estimator (Peña et al. (2001)). However, if data are correlated, other models should be used such as frailty models or an estimator proposed by Wang and Chang (1999), that take into account the fact that interocurrence times were or not correlated. The aim of this paper has been the illustration of these approaches by using two real data sets.

[Non-parametric estimation of survival function for recurrent events data].