Zobrazeno 1 - 10
of 193
pro vyhledávání: '"Psioda, Matthew A."'
The BayesPPDSurv (Bayesian Power Prior Design for Survival Data) R package supports Bayesian power and type I error calculations and model fitting using the power and normalized power priors incorporating historical data with for the analysis of time
Externí odkaz:
http://arxiv.org/abs/2404.05118
The power prior is a popular class of informative priors for incorporating information from historical data. It involves raising the likelihood for the historical data to a power, which acts as a discounting parameter. When the discounting parameter
Externí odkaz:
http://arxiv.org/abs/2404.02453
Autor:
Kwiatkowski, Evan, Zhu, Jiawen, Li, Xiao, Pang, Herbert, Lieberman, Grazyna, Psioda, Matthew A.
We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCT) where the degree of borrowing is determined based on similarity between RCT and external control patients to ac
Externí odkaz:
http://arxiv.org/abs/2305.05913
The power prior is a popular class of informative priors for incorporating information from historical data. It involves raising the likelihood for the historical data to a power, which acts as discounting parameter. When the discounting parameter is
Externí odkaz:
http://arxiv.org/abs/2302.14230
The R package BayesPPD (Bayesian Power Prior Design) supports Bayesian power and type I error calculation and model fitting after incorporating historical data with the power prior and the normalized power prior for generalized linear models (GLM). T
Externí odkaz:
http://arxiv.org/abs/2112.14616
There has been increased interest in using prior information in statistical analyses. For example, in rare diseases, it can be difficult to establish treatment efficacy based solely on data from a prospective study due to low sample sizes. To overcom
Externí odkaz:
http://arxiv.org/abs/2107.11195
We develop the scale transformed power prior for settings where historical and current data involve different data types, such as binary and continuous data, respectively. This situation arises often in clinical trials, for example, when historical d
Externí odkaz:
http://arxiv.org/abs/2105.05157
Given the cost and duration of phase III and phase IV clinical trials, the development of statistical methods for go/no-go decisions is vital. In this paper, we introduce a Bayesian methodology to compute the probability of success based on the curre
Externí odkaz:
http://arxiv.org/abs/2010.13774
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