Safety signal detection with control of latent factors.
Stat Med
; 43(7): 1397-1418, 2024 Mar 30.
Article
en En
| MEDLINE
| ID: mdl-38297431
ABSTRACT
Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Vacunas
/
Sistemas de Registro de Reacción Adversa a Medicamentos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Stat Med
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos