Variable selection in high dimensions for discrete-outcome individualized treatment rules: Reducing severity of depression symptoms.
Biostatistics
; 2023 Aug 31.
Article
en En
| MEDLINE
| ID: mdl-37660312
ABSTRACT
Despite growing interest in estimating individualized treatment rules, little attention has been given the binary outcome setting. Estimation is challenging with nonlinear link functions, especially when variable selection is needed. We use a new computational approach to solve a recently proposed doubly robust regularized estimating equation to accomplish this difficult task in a case study of depression treatment. We demonstrate an application of this new approach in combination with a weighted and penalized estimating equation to this challenging binary outcome setting. We demonstrate the double robustness of the method and its effectiveness for variable selection. The work is motivated by and applied to an analysis of treatment for unipolar depression using a population of patients treated at Kaiser Permanente Washington.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
Idioma:
En
Revista:
Biostatistics
Año:
2023
Tipo del documento:
Article