Survival analysis with complex covariates: a model-based clustering preprocessing step

Vandewalle, Vincent; Biernacki, Christophe

Type de document :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes: Conférence invitée

Titre :

Survival analysis with complex covariates: a model-based clustering preprocessing step

Auteur(s) :

Vandewalle, Vincent [Auteur]

MOdel for Data Analysis and Learning [MODAL]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Université de Lille, Droit et Santé
Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Université de Lille, Sciences et Technologies

Titre de la manifestation scientifique :

IEEE PHM 2017

Ville :

Dallas

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2017-06-19

Discipline(s) HAL :

Statistiques [stat]

Résumé en anglais : [en]

Many covariates are now available through sensors in the industrial context, and are expected to be related to the survival analysis target. Such covariates are often complex, what has to be understood as a possible mix ...
Lire la suite >Many covariates are now available through sensors in the industrial context, and are expected to be related to the survival analysis target. Such covariates are often complex, what has to be understood as a possible mix between continuous, categorical, even functional over time, variables with the possibility to contain missing or uncertain values. A natural question in survival analysis is to design in both flexible and easy way an hazard function related to these potentially complex covariates, while preserving the opportunity to benefit from classical hazard functions. In this tutorial, we will propose to decompose this unknown targeted hazard function into two complementary parts. The first one can be any classical user hazard function conditional on a latent categorical variable. The second one is the distribution of this latent variable conditionally to the complex covariates. The way to combine both parts is to sum their product over the latent variable (marginal distribution), leading to the final targeted hazard function.The key to perform this approach is to focus on the latent variable definition which can be obtained with a model based clustering approach dedicated to complex covariates. In this tutorial we will give a selected review of recent methodologies dedicated to clustering. Beyond methodology, we will describe in depth some related software to perform previous clustering methods. Some case studies will be also provided in an industrial context. At the end of the talk the practitioner will be thus able to perform such clustering method to use it finally with its own hazard function.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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