Titre :

A functional-model-adjusted spatial scan statistic.

Auteur(s) :

AHMED, Mohamed-Salem [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Genin, Michaël [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Titre de la revue :

Statistics in Medicine

Nom court de la revue :

Stat Med

Numéro :

39

Pagination :

1025-1040

Date de publication :

2020-01-27

ISSN :

1097-0258

Mot(s)-clé(s) en anglais :

cluster detection
confounding factor
functional data analysis
generalized functional linear model
longitudinal data

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

This paper introduces a new spatial scan statistic designed to adjust cluster detection for longitudinal confounding factors indexed in space. The functional-model-adjusted statistic was developed using generalized functional ...
Lire la suite >This paper introduces a new spatial scan statistic designed to adjust cluster detection for longitudinal confounding factors indexed in space. The functional-model-adjusted statistic was developed using generalized functional linear models in which longitudinal confounding factors were considered to be functional covariates. A general framework was developed for application to various probability models. Application to a Poisson model showed that the new method is equivalent to a conventional spatial scan statistic that adjusts the underlying population for covariates. In a simulation study with single and multiple covariate models, we found that our new method adjusts the cluster detection procedure more accurately than other methods. Use of the new spatial scan statistic was illustrated by analyzing data on premature mortality in France over the period from 1998 to 2013, with the quarterly unemployment rate as a longitudinal confounding factor.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CHU Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2023-11-15T09:31:57Z
2024-01-08T15:58:05Z