Improved inference for MCP-Mod approach using time-to-event endpoints with small sample sizes.
Pharm Stat
; 22(5): 760-772, 2023.
Article
en En
| MEDLINE
| ID: mdl-37119000
ABSTRACT
The Multiple Comparison Procedures with Modeling Techniques (MCP-Mod) framework has been recently approved by the U.S. Food, Administration, and European Medicines Agency as fit-for-purpose for phase II studies. Nonetheless, this approach relies on the asymptotic properties of Maximum Likelihood (ML) estimators, which might not be reasonable for small sample sizes. In this paper, we derived improved ML estimators and correction for their covariance matrices in the censored Weibull regression model based on the corrective and preventive approaches. We performed two simulation studies to evaluate ML and improved ML estimators with their covariance matrices in (i) a regression framework (ii) the Multiple Comparison Procedures with Modeling Techniques framework. We have shown that improved ML estimators are less biased than ML estimators yielding Wald-type statistics that controls type I error without loss of power in both frameworks. Therefore, we recommend the use of improved ML estimators in the MCP-Mod approach to control type I error at nominal value for sample sizes ranging from 5 to 25 subjects per dose.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Tamaño de la Muestra
Límite:
Humans
Idioma:
En
Revista:
Pharm Stat
Asunto de la revista:
FARMACOLOGIA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos