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Improved inference for MCP-Mod approach for time-to-event endpoints with small sample sizes | | Márcio A. Diniz
; Diego I. Gallardo
; Tiago M. Magalhães
; | | Date: |
1 Jan 2023 | | Abstract: | The Multiple Comparison Procedures with Modeling Techniques (MCP-Mod)
framework has been recently approved by the U.S. Food and Administration and
European Medicines Agency as fit-per-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. | | Source: | arXiv, 2301.00325 | | Services: | Forum | Review | PDF | Favorites | |
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