Statistical models for longitudinal biomarkers of disease onset.
Stat Med
; 19(4): 617-37, 2000 Feb 29.
Article
en En
| MEDLINE
| ID: mdl-10694740
We consider the analysis of serial biomarkers to screen and monitor individuals in a given population for onset of a specific disease of interest. The biomarker readings are subject to error. We survey some of the existing literature and concentrate on two recently proposed models. The first is a fully Bayesian hierarchical structure for a mixed effects segmented regression model. Posterior estimates of the changepoint (onset time) distribution are obtained by Gibbs sampling. The second is a hidden changepoint model in which the onset time distribution is estimated by maximum likelihood using the EM algorithm. Both methods lead to a dynamic index that represents a strength of evidence that onset has occurred by the current time in an individual subject. The methods are applied to some large data sets concerning prostate specific antigen (PSA) as a serial marker for prostate cancer. Rules based on the indices are compared to standard diagnostic criteria through the use of ROC curves adapted for longitudinal data.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Biomarcadores
/
Modelos Estadísticos
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
/
Male
Idioma:
En
Revista:
Stat Med
Año:
2000
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Reino Unido