Λέσχη Φίλων Στατιστικής - GrStats forum
AUEB Stats Seminars 20/10/2023: Joint modeling of longitudinal and competing-risk data using cumulative incidence functions accounting for failure cause misclassification by Christos Thomadakis (Adjunct Lecturer, Department of Statistics, AUEB) Forumgrstats

Join the forum, it's quick and easy

Λέσχη Φίλων Στατιστικής - GrStats forum
AUEB Stats Seminars 20/10/2023: Joint modeling of longitudinal and competing-risk data using cumulative incidence functions accounting for failure cause misclassification by Christos Thomadakis (Adjunct Lecturer, Department of Statistics, AUEB) Forumgrstats
Λέσχη Φίλων Στατιστικής - GrStats forum
Would you like to react to this message? Create an account in a few clicks or log in to continue.
Για προβλήματα εγγραφής και άλλες πληροφορίες επικοινωνήστε με : grstats.forum@gmail.com ή grstats@stat-athens.aueb.gr

Go down
avatar
GRStats2
Posts : 56
Join date : 2022-11-19

AUEB Stats Seminars 20/10/2023: Joint modeling of longitudinal and competing-risk data using cumulative incidence functions accounting for failure cause misclassification by Christos Thomadakis (Adjunct Lecturer, Department of Statistics, AUEB) Empty AUEB Stats Seminars 20/10/2023: Joint modeling of longitudinal and competing-risk data using cumulative incidence functions accounting for failure cause misclassification by Christos Thomadakis (Adjunct Lecturer, Department of Statistics, AUEB)

Wed 18 Oct 2023 - 17:57
AUEB STATS SEMINARS 2023

Christos Thomadakis
Adjunct Lecturer, Department of Statistics, AUEB

Title: Joint modeling of longitudinal and competing-risk data using cumulative incidence functions accounting for failure cause misclassification

FRIDAY, 20/10/2023
13:15

Room T105, Troias Building

ABSTRACT

Most of the literature on joint modeling of longitudinal and competing-risk data is based on cause-specific hazards, although modeling of the cumulative incidence function (CIF) is an easier and more direct approach to evaluate the prognosis of an event. We propose a flexible class of shared parameter models to jointly model a normally distributed marker over time and multiple causes of failure using CIFs for the survival submodels, with CIFs depending on the “true” marker value over time (i.e., removing the measurement error). The generalized odds rate transformation is applied, thus a proportional subdistribution hazards model is a special case. The requirement that the all-cause CIF should be bounded by 1 is formally considered. The proposed models are extended to account for potential failure cause misclassification, where the true failure causes are available in a small random sample of individuals. We also provide a multistate representation of the whole population by defining mutually exclusive states based on the marker values and the competing risks. Based solely on the assumed joint model, we derive fully Bayesian posterior samples for state occupation and transition probabilities. The proposed approach is evaluated in a simulation study and, as an illustration, it is fitted to real data from people with HIV.

AUEB Stats Seminars 20/10/2023: Joint modeling of longitudinal and competing-risk data using cumulative incidence functions accounting for failure cause misclassification by Christos Thomadakis (Adjunct Lecturer, Department of Statistics, AUEB) 1697033145296?e=1700697600&v=beta&t=PL88fLLul9NWBDkh16daeLKZV5mRqm5ugC33vWkdHCw
Back to top
Permissions in this forum:
You cannot reply to topics in this forum