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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The analysis of multiple time-to-event outcomes in a randomised controlledclinical trial can be accomplished with exisiting methods. However, dependingon the characteristics of the disease under investigation and the circumstancesin which the study is planned, it may be of interest to conduct interimanalyses and adapt the study design if necessary. Due to the expecteddependency of the endpoints, the full available information on the involvedendpoints may not be used for this purpose. We suggest a solution to thisproblem by embedding the endpoints in a multi-state model. If this model isMarkovian, it is possible to take the disease history of the patients intoaccount and allow for data-dependent design adaptiations. To this end, weintroduce a flexible test procedure for a variety of applications, but areparticularly concerned with the simultaneous consideration of progression-freesurvival (PFS) and overall survival (OS). This setting is of key interest inoncological trials. We conduct simulation studies to determine the propertiesfor small sample sizes and demonstrate an application based on data from theNB2004-HR study.

Confirmatory adaptive group sequential designs for clinical trials with multiple time-to-event outcomes in Markov models