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The traditional prescribing approaches in clinical therapies, such as “one drug fits all”, have limits due to consideration of drug effectiveness and safety. It is now well recognized that the Single Nucleotide Polymorphism (SNP) plays an important role in pharmacogenomics and personalized medicine by serving as the predictive biomarker for patient stratification and dose selection. Considering the economic efficiency of drug development, searching for the single best predictive SNP has just begun and has great potential. Current statistical methods for the best predictive biomarker selection rely on a variety of ranking procedures. However, these ordering approaches face three main issues: (1) they can potentially fail to distinguish predictive biomarkers from prognostic biomarkers; (2) the ordering is not necessarily correlated with the true significance, especially when the signal-to-noise is small; (3) such ranking approaches provide no control on the incorrect assertion in terms of the best predictive SNP detection.In this paper, we propose to overcome the first issue by quantifying the predictive ability of each candidate SNP in two parallel approaches: (1) adjusting the model with Least Square mean (LSmean); (2) adjusting the data with Virtual Matching (VM) before doing estimation. For the selection of the best predictive SNP, we propose to apply the Multiple Comparisons with the Best (MCB) to the ranking and selection procedures. Specifically, we will do the indifference zone selection with MCB lower bounds; and the subset selection with MCB upper bounds (suppose a larger response is better). The probability that both inferences are correct is controlled. Simulation studies show that our proposed method is more reliable in distinguishing between predictive and prognostic effects, and has a larger chance to detect the true best predictive SNP than varieties of methods in both multiple testing and machine learning approaches. Furthermore, the adjusting-model a

The traditional prescribing approaches in clinical therapies, such as “one drug fits all”, have limits due to consideration of drug effectiveness and safety. It is now well recognized that the Single Nucleotide Polymorphism (SNP) plays an important role in pharmacogenomics and personalized medicine by serving as the predictive biomarker for patient stratification and dose selection. Considering the economic efficiency of drug development, searching for the single best predictive SNP has just begun and has great potential. Current statistical methods for the best predictive biomarker selection rely on a variety of ranking procedures. However, these ordering approaches face three main issues: (1) they can potentially fail to distinguish predictive biomarkers from prognostic biomarkers; (2) the ordering is not necessarily correlated with the true significance, especially when the signal-to-noise is small; (3) such ranking approaches provide no control on the incorrect assertion in terms of the best predictive SNP detection.In this paper, we propose to overcome the first issue by quantifying the predictive ability of each candidate SNP in two parallel approaches: (1) adjusting the model with Least Square mean (LSmean); (2) adjusting the data with Virtual Matching (VM) before doing estimation. For the selection of the best predictive SNP, we propose to apply the Multiple Comparisons with the Best (MCB) to the ranking and selection procedures. Specifically, we will do the indifference zone selection with MCB lower bounds; and the subset selection with MCB upper bounds (suppose a larger response is better). The probability that both inferences are correct is controlled. Simulation studies show that our proposed method is more reliable in distinguishing between predictive and prognostic effects, and has a larger chance to detect the true best predictive SNP than varieties of methods in both multiple testing and machine learning approaches. Furthermore, the adjusting-model a

Multinomial data is present when the outcome of an experiment is a discrete choice of more than two mutually exclusive alternatives and a multinomial distribution is assumed as the underlying distribution. Usually, a multinomial regression model is considered for the analysis of multinomial count data. Frequently, such data exhibits overdispersion especially if the data is acquired in clusters. The collection of data in cell cultures, litters, members of a family or classroom will lead to observations that are more similar within clusters than observations from di erent clusters. Therefore, it has to be expected that some sources of variation may di er between clusters and overdispersion is present. In addition, several treatments are often of interest for such data, causing a multiple testing problem. While for normally distributed data multiple comparison procedures proposed by Tukey and Dunnett are standard since decades, multiple treatment comparisons between several overdispersed multinomial samples have been rarely investigated. The primary objective of this thesis is to develop a method to obtain multiple hypothesis tests and simultaneous confidence intervals for the comparison of multiple polytomous vectors that lack independence among experimental units due to a collection of data in clusters. Building on previous work, overdispersion is considered and an asymptotic procedure is proposed for simultaneous inference of odds ratios between multiple multinomial samples by including an estimated dispersion parameter. To assess validity, a simulation approach utilizing the Dirichlet-multinomial distribution is applied to determine the simultaneous coverage probability of confidence intervals for different magnitudes of overdispersion. As part of this thesis, the proposed test procedure is implemented in the statistical software environment R in a user-friendly way. The application of the novel method and corresponding R-functions is described comprehensively on t

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The analysis of dose-response relationships is a common problem in pre-clinical studies. For example, proportions such as mortality rates and histopathological findings are of particular interest in repeated toxicity studies. Commonly applied designs consist of an untreated control group and several, possibly unequally spaced, dosage groups. The Williams test can be formulated as a multiple contrast test and is a powerful option to evaluate such data. In this paper, we consider simultaneous inference for Williams-type multiple contrasts when the response variable is binomial and sample sizes are only moderate. Approximate simultaneous confidence limits can be constructed using the quantiles of a multivariate normal distribution taking the correlation into account. Alternatively, multiplicity-adjusted p-values can be calculated as well. A simulation study shows that a simple correction based on adding pseudo observations leads to acceptable performance for moderate sample sizes, such as 40 per group. In addition, the calculation of adjusted p-values and approximate power is presented. Finally, the proposed methods are applied to example data from two toxicological studies, the methods are available in an R-package. © 2010 The Berkeley Electronic Press. All rights reserved.