Meta-analysis methods and models with applications in evaluation of cholesterol-lowering drugs.
Stat Med
; 31(28): 3597-616, 2012 Dec 10.
Article
en En
| MEDLINE
| ID: mdl-22829358
ABSTRACT
In this paper, we propose a class of multivariate random effects models allowing for the inclusion of study-level covariates to carry out meta-analyses. As existing algorithms for computing maximum likelihood estimates often converge poorly or may not converge at all when the random effects are multi-dimensional, we develop an efficient expectation-maximization algorithm for fitting multi-dimensional random effects regression models. In addition, we also develop a new methodology for carrying out variable selection with study-level covariates. We examine the performance of the proposed methodology via a simulation study. We apply the proposed methodology to analyze metadata from 26 studies involving statins as a monotherapy and in combination with ezetimibe. In particular, we compare the low-density lipoprotein cholesterol-lowering efficacy of monotherapy and combination therapy on two patient populations (naïve and non-naïve patients to statin monotherapy at baseline), controlling for aggregate covariates. The proposed methodology is quite general and can be applied in any meta-analysis setting for a wide range of scientific applications and therefore offers new analytic methods of clinical importance.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Ensayos Clínicos Controlados Aleatorios como Asunto
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Metaanálisis como Asunto
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Hipercolesterolemia
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Anticolesterolemiantes
Tipo de estudio:
Clinical_trials
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Diagnostic_studies
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Evaluation_studies
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Prognostic_studies
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Systematic_reviews
Límite:
Humans
Idioma:
En
Revista:
Stat Med
Año:
2012
Tipo del documento:
Article
País de afiliación:
Estados Unidos