We introduce a Bayesian framework centered on the “probability of decision” for designing dose-finding trials. The proposed PoD-BIN design evaluates the posterior predictive probabilities of up-and-down decisions. I...
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In bioinformatics, the rapid development of sequencing technology has enabled us to collect an increasing amount of omics data. Classification based on omics data is one of the central problems in biomedical research....
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In bioinformatics, the rapid development of sequencing technology has enabled us to collect an increasing amount of omics data. Classification based on omics data is one of the central problems in biomedical research. However, omics data usually has a limited sample size but high feature dimensions, and it is assumed that only a few features (biomarkers) are active, i.e. informative to discriminate between different categories. Identifying active biomarkers for classification has therefore become fundamental for omics data analysis. Focusing on binary classification, we propose an innovative feature selection method aiming at dealing with the high correlations between the biomarkers. Our method, WLogit, consists in whitening the design matrix to remove the correlations between biomarkers, then using a penalized criterion adapted to the logistic regression model to select features. The results from numerical experiments suggest that WLogit can identify almost all active biomarkers even in the cases where the biomarkers are highly correlated, while the other methods fail, which consequently leads to higher classification accuracy. The performance of WLogit is also evaluated on two publicly available datasets, and the obtained classifier outperformed other methods in terms of prediction accuracy. Our method is implemented in the WLogit R package available from the Comprehensive R Archive Network (CRAN).
The sample size requirement in a thorough QT/QTc study is discussed under a balanced parallel or crossover study design. First, we explore the impacts of various factors on the study power, including the mean effect p...
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The sample size requirement in a thorough QT/QTc study is discussed under a balanced parallel or crossover study design. First, we explore the impacts of various factors on the study power, including the mean effect profile across time and correlation among time points. Then we estimate the variability parameters needed based on multiple historical studies. Different baseline usage is illustrated to have a significant impact on the analysis variability in the parallel studies. Finally, the sample size calculations and recommendations are given for demonstrating a “negative” drug effect and the study assay sensitivity, respectively. [ABSTRACT FROM AUTHOR]
Research in oncology has changed the focus from histological properties of tumors in a specific organ to a specific genomic aberration potentially shared by multiple cancer types. This motivates the basket trial, whic...
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The objective of the present study was to determine whether vitamin D intake is associated with CVD mortality in a general population sample. The association between vitamin D intake and CVD mortality (ICD-9 code 4104...
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The primary objective of phase I oncology studies is to establish the safety profile of a new treatment and determine the maximum tolerated dose (MTD). This is motivated by the development of cytotoxic agents based on...
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Cardiovascular event trials can be designed to allow adaptation based on interim results. In this article, we first discuss a strategy either to accept noninferiority at an interim analysis or to continue the study to...
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