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Title: Factorial survival analysis for treatment effects under dependent censoring Authors:  Takeshi Emura - Kurume University (Japan) [presenting]
Dennis Dobler - University of Duesseldorf (Germany)
Marc Ditzhaus - Otto-von-Guericke University Magdeburg (Germany)
Abstract: Factorial analyses offer a powerful nonparametric means to detect main or interaction effects among multiple treatments. For survival outcomes, e.g. from clinical trials or animal experiments, such techniques can be adopted for comparing reasonable quantifications of treatment effects. The key difficulty to solve in survival analysis concerns the proper handling of censoring. So far, all existing factorial analyses in survival data were developed under the independent censoring assumption, which is too strong for many applications. As a solution, the central aim is to develop new methods in factorial survival analyses under quite a general dependent censoring regimes. This will be accomplished by combining existing results for factorial survival analyses with techniques developed for survival copula models. As a result, we will present an appealing F-test which exhibits good performance in our simulation study and is illustrated in a real data analysis.