A functional-model-adjusted spatial scan statistic.
Stat Med
; 39(8): 1025-1040, 2020 04 15.
Article
in En
| MEDLINE
| ID: mdl-31965600
ABSTRACT
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.
Key words
Full text:
1
Database:
MEDLINE
Main subject:
Models, Statistical
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
Europa
Language:
En
Journal:
Stat Med
Year:
2020
Type:
Article
Affiliation country:
France