Time-varying proportional odds model for mega-analysis of clustered event times.
Biostatistics
; 20(1): 129-146, 2019 01 01.
Article
en En
| MEDLINE
| ID: mdl-29309509
ABSTRACT
Mega-analysis, or the meta-analysis of individual data, enables pooling and comparing multiple studies to enhance estimation and power. A challenge in mega-analysis is estimating the distribution for clustered, potentially censored event times where the dependency structure can introduce bias if ignored. We propose a new proportional odds model with unknown, time-varying coefficients, and random effects. The model directly captures event dependencies, handles censoring using pseudo-values, and permits a simple estimation by transforming the model into an easily estimable additive logistic mixed effect model. Our method consistently estimates the distribution for clustered event times even under covariate-dependent censoring. Applied to three observational studies of Huntington's disease, our method provides, for the first time in the literature, evidence of similar conclusions about motor and cognitive impairments in all studies despite different recruitment criteria.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Metaanálisis como Asunto
/
Modelos Estadísticos
Tipo de estudio:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
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Systematic_reviews
Límite:
Humans
Idioma:
En
Revista:
Biostatistics
Año:
2019
Tipo del documento:
Article