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Editor’s Choice : Power Calculations for Two-Wave, Change from Baseline to Follow-Up Study Designs
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Abstract: Change in a quantitative trait is commonly employed as an endpoint in two-wave longitudinal studies. For example, early phase clinical trials often use two-wave designs with biomarker endpoints to confirm that a treatment affects the putative target treatment pathway before proceeding to larger scale clinical efficacy trials. Power calculations for such designs are straightforward if pilot data from longitudinal investigations of similar duration to the proposed study are available. Often longitudinal pilot data of similar duration are not available, and simplifying assumptions are used to calculate sample size from cross-sectional data, one standard approach being to use a formula based on variance estimated from cross sectional data and correlation estimates abstracted from the literature or inferred from experience with similar endpoints. An implicit assumption of this standard approach is that the variance of the quantitative trait is the same at baseline and follow-up. In practice, this assumption rarely holds, and sample size estimates by this standard formula can be dramatically anti-conservative. Even when longitudinal pilot data for estimating parameters required in sample size calculations are available, sample size calculations will be biased if the interval from baseline to follow-up is not of similar duration to that proposed for the study being designed. In this paper we characterize the magnitude of bias in sample size estimates when formula assumptions do not hold and derive alternative conservative formulas for sample size required to achieve nominal power. Keywords: Sample Size, Phase 2, Phase II, Clinical Trial, Rate of Change, Compound Symmetr.Download Full Article |
Editor’s Choice : Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data
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Abstract: In this paper, we present a Bayesian framework for analyzing longitudinal ordinal response data. In analyzing longitudinal data, the possibility of correlations between responses given by the same individual needs to be taken into account. Various models can be used to handle such correlations such as marginal modeling, random effect modeling and transition (Markov) modeling. Here a transition modeling is used and a Bayesian approach is presented for analyzing longitudinal data. A cumulative logistic regression model and the Bayesian method, using MCMC, are implemented for obtaining the parameters estimates. Our approach is applied on a two-period longitudinal Insomnia data where the Bayesian estimate for measure of association, , between the initial and follow-up ordinal responses is obtained in each level of a treatment variable. Then, the sensitivity of posterior summaries to changes of prior hyperparameters is investigated. We also use Bayes factor criterion for testing some important hypotheses. Keywords: Bayesian Analysis, Bayes Factor, Conditional Predictive Ordinate, Logistic Regression, Markov Model.Download Full Article |
Editor’s Choice : Validation of Gene Expression Profiles in Genomic Data through Complementary Use of Cluster Analysis and PCA-Related Biplots
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Abstract: High-throughput genomic assays are used in molecular biology to explore patterns of joint expression of thousands of genes. These methodologies had relevant developments in the last decade, and concurrently there was a need for appropriate methods for analyzing the massive data generated. Identifying sets of genes and samples characterized by similar values of expression and validating these results are two critical issues related to these investigations because of their clinical implication. From a statistical perspective, unsupervised class discovery methods like Cluster Analysis are generally adopted. However, the use of Cluster Analysis mainly relies on the use of hierarchical techniques without considering possible use of other methods. This is partially due to software availability and to easiness of representation of results through a heatmap, which allows to simultaneously visualize clusterization of genes and samples on the same graphical device. One drawback of this strategy is that clusters’ stability is often neglected, thus leading to over-interpretation of results. Moreover, validation of results using external datasets is still subject of discussion, since it is well known that batch effects may condition gene expression results even after normalization. In this paper we compared several clustering algorithms (hierarchical, k-means, model-based, Affinity Propagation) and stability indices to discover common patterns of expression and to assess clustering reliability, and propose a rank-based passive projection of Principal Components for validation purposes. Results from a study involving 23 tumor cell lines and 76 genes related to a specific biological pathway and derived from a publicly available dataset, are presented. Keywords: Microarrays, cluster stability, multivariate visualization, Principal Components Analysis, cell polarity.Download Full Article |
