Toorawa, Robert Adena, Michael Donovan, Mark Jones, Steve Conlon, John Use of simulation to compare the performance of minimization with stratified blocked randomization. Copyright Â© 2010 John Wiley & Sons, Ltd.

The impact of randomization on predicted drug supply overage is discussed. The influence of patient dropout is also investigated. It is shown that the loss of statistical power is practically negligible and can be compensated by a minor increase in sample size. The impact of imbalance on the power of the study is considered. In the case of two treatments, the properties of the total imbalance in the number of patients on treatment arms caused by using centre- stratified randomization are investigated and for a large number of centres a normal approximation of imbalance is proved. Closed-form expressions for corresponding distributions of the predicted number of the patients randomized in different regions are derived. A new analytic approach using a Poisson-gamma patient recruitment model (patients arrive at different centres according to Poisson processes with rates sampled from a gamma distributed population) and its further extensions is proposed. The prediction of the number of patients randomized to different treatment arms in different regions during the recruitment period accounting for the stochastic nature of the recruitment and effects of multiple centres is investigated. The two randomization schemes most often used in clinical trials are considered: unstratified and centre- stratified block-permuted randomization. This paper deals with the analysis of randomization effects in multi-centre clinical trials. For Parkinson data, we also show some interesting genes identified by the method, which may be associated with neurological disorders for further biological investigations.Įffects of unstratified and centre- stratified randomization in multi-centre clinical trials. We employ two genome-wide SNP data sets (Parkinson case-control data comprised of 408â€‰803 SNPs and Alzheimer case-control data comprised of 380â€‰157 SNPs) to demonstrate that the proposed stratified sampling method is effective, and it can generate better random forest with higher accuracy and lower error bound than those by Breiman's random forest generation method. The advantage of this stratified sampling procedure can make sure each subspace contains enough useful SNPs, but can avoid a very high computational cost of exhaustive search of an optimal mtry, and maintain the randomness of a random forest. In feature subspace selection, we randomly select the same number of SNPs from each group and combine them to form a subspace to generate a decision tree.

Our idea is to design an equal-width discretization scheme for informativeness to divide SNPs into multiple groups. The main aim of this paper is to propose a stratified sampling method for feature subspace selection to generate decision trees in a random forest for GWA high-dimensional data.

However, it is too time-consuming and not favorable in GWA for high-dimensional data. Exhaustive searching an optimal mtry is often required in order to include useful and relevant SNPs and get rid of vast of non-informative SNPs. A simple random sampling method in random forest using default mtry parameter to choose feature subspace, will select too many subspaces without informative SNPs. Wu, Qingyao Ye, Yunming Liu, Yang Ng, Michael Kįor high dimensional genome-wide association (GWA) case-control data of complex disease, there are usually a large portion of single-nucleotide polymorphisms (SNPs) that are irrelevant with the disease. SNP selection and classification of genome-wide SNP data using stratified sampling random forests.

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