FARMS: A New Algorithm for Variable Selection.
Biomed Res Int
; 2015: 319797, 2015.
Article
em En
| MEDLINE
| ID: mdl-26273608
Large datasets including an extensive number of covariates are generated these days in many different situations, for instance, in detailed genetic studies of outbreed human populations or in complex analyses of immune responses to different infections. Aiming at informing clinical interventions or vaccine design, methods for variable selection identifying those variables with the optimal prediction performance for a specific outcome are crucial. However, testing for all potential subsets of variables is not feasible and alternatives to existing methods are needed. Here, we describe a new method to handle such complex datasets, referred to as FARMS, that combines forward and all subsets regression for model selection. We apply FARMS to a host genetic and immunological dataset of over 800 individuals from Lima (Peru) and Durban (South Africa) who were HIV infected and tested for antiviral immune responses. This dataset includes more than 500 explanatory variables: around 400 variables with information on HIV immune reactivity and around 100 individual genetic characteristics. We have implemented FARMS in R statistical language and we showed that FARMS is fast and outcompetes other comparable commonly used approaches, thus providing a new tool for the thorough analysis of complex datasets without the need for massive computational infrastructure.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Antivirais
/
Infecções por HIV
/
Imunidade
Tipo de estudo:
Prognostic_studies
Limite:
Humans
País/Região como assunto:
Africa
/
America do sul
/
Peru
Idioma:
En
Revista:
Biomed Res Int
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Espanha
País de publicação:
Estados Unidos