Conditional Monte Carlo randomization tests for regression models.
Stat Med
; 33(18): 3078-88, 2014 Aug 15.
Article
en En
| MEDLINE
| ID: mdl-24648378
ABSTRACT
We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Ensayos Clínicos Controlados Aleatorios como Asunto
Tipo de estudio:
Clinical_trials
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Diagnostic_studies
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Health_economic_evaluation
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Stat Med
Año:
2014
Tipo del documento:
Article
País de afiliación:
Estados Unidos