FABIA: factor analysis for bicluster acquisition

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called ‘FABIA: Factor Analysis for Bicluster Acquisition’. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques

Using Linear Mixed Models for Normalization of cDNA Microarrays

Microarrays are a tool for measuring the expression levels of a large number of genes simultaneously. In the microarray experiment, however, many undesirable systematic variations are observed. Correct identification and removal of these variations is essential to allow the comparison of expression levels across experiments. We describe the use of linear mixed models for the normalization of two-color spotted microarrays for various sources of variation including printtip variation. Normalization with linear mixed models provides a parametric model of which results compare favorably to intensity dependent normalization LOWESS methods. We illustrate the use of this technique on two datasets. The first dataset contains 24 arrays, each with approximately 600 genes, replicated 3 times per array. A second dataset, coming from the apo AI experiment, was used to further illustrate the methods. Finally, a simulation study was done to compare between methods.

A modeling approach to the analysis of nerve regenerative experiments

Many experiments aiming at the investigation of nerve repair involve elaborate testing over a certain time period. Data arising from such experiments are often analyzed by time-point. Such a cross-sectional approach is often very inefficient. In this paper, we consider a case study in which repeated measurements of two response variables assumed to be Poisson distributed are obtained. We show how a repeated measures modeling approach, based on generalized linear models, can handle both responses in one model and improve the inference in the nerve repair experiments. The benefits of the model as well as problems that can occur are illustrated and discussed.status: publishe

Comparative Efficacy of Ibrutinib Versus Obinutuzumab + Chlorambucil in First-Line Treatment of Chronic Lymphocytic Leukemia: A Matching-Adjusted Indirect Comparison

<p><strong>Article full text</strong></p> <p><br> The full text of this article can be found <a href="https://link.springer.com/article/10.1007/s12325-017-0564-1"><b>here</b>.</a><br> <br> <strong>Provide enhanced digital features for this article</strong><br> If you are an author of this publication and would like to provide additional enhanced digital features for your article then please contact <u>[email protected]</u>.<br> <br> The journal offers a range of additional features designed to increase visibility and readership. All features will be thoroughly peer reviewed to ensure the content is of the highest scientific standard and all features are marked as ‘peer reviewed’ to ensure readers are aware that the content has been reviewed to the same level as the articles they are being presented alongside. Moreover, all sponsorship and disclosure information is included to provide complete transparency and adherence to good publication practices. This ensures that however the content is reached the reader has a full understanding of its origin. No fees are charged for hosting additional open access content.<br> <br> Other enhanced features include, but are not limited to:<br> • Slide decks<br> • Videos and animations<br> • Audio abstracts<br> • Audio slides<u></u></p

An Indirect Comparison of Changes in the Impact of Weight on Quality of Life Among Subjects with Type 2 Diabetes Treated with Antihyperglycemic Agents in Dual Therapy with Metformin

<p><b> </b></p> <p><b> </b></p> <p><b>Article full text</b></p> <p><br></p> <p>The full text of this article can be found <a href="https://link.springer.com/article/10.1007/s13300-017-0343-7"><b>here</b>.</a></p> <p><br></p> <p><b>Provide enhanced content for this article</b></p> <p><br></p> <p>If you are an author of this publication and would like to provide additional enhanced content for your article then please contact <a href="http://www.medengine.com/Redeem/”mailto:[email protected]”"><b>[email protected]</b></a>.</p> <p> </p> <p>The journal offers a range of additional features designed to increase visibility and readership. All features will be thoroughly peer reviewed to ensure the content is of the highest scientific standard and all features are marked as ‘peer reviewed’ to ensure readers are aware that the content has been reviewed to the same level as the articles they are being presented alongside. Moreover, all sponsorship and disclosure information is included to provide complete transparency and adherence to good publication practices. This ensures that however the content is reached the reader has a full understanding of its origin. No fees are charged for hosting additional open access content.</p> <p><br></p> <p>Other enhanced features include, but are not limited to:</p> <p><br></p> <p>• Slide decks</p> <p>• Videos and animations</p> <p>• Audio abstracts</p> <p>• Audio slides</p> <p> </p> <p> </p

Matching-adjusted indirect comparison (MAIC) results confirmed by head-to-head trials: a case study in psoriasis

Background Head-to-head comparisons through randomized controlled trials (RCTs) provide high-quality evidence to inform healthcare decisions. In their absence, indirect comparisons are often performed; however, evidence is limited on how valid matching-adjusted indirect comparison (MAIC)–based comparative efficacy estimates are vs. RCT-based estimates. Objectives Compare MAIC and RCT results of guselkumab vs. secukinumab and ixekizumab to provide insight into the validity of results generated using MAIC methods. Methods Previously reported results from MAICs of guselkumab vs. secukinumab and ixekizumab were compared with results from ECLIPSE and IXORA-R RCTs based on risk differences between Psoriasis Area and Severity Index (PASI) 90 response rates. Results Risk difference (95% confidence interval) in PASI 90 response rates at week 48 for guselkumab vs. secukinumab was 14.4% (9.4%; 19.4%) in ECLIPSE and 9.4% (4.7%; 14.0%) in the MAIC. The risk difference at week 24 for guselkumab vs. ixekizumab was 0.0% (−5.4%; 5.4%) in IXORA-R and 0.7% (−5.1%; 6.4%) in the MAIC. Conclusions Comparative efficacy results were consistent between MAICs and RCTs of guselkumab vs. secukinumab and ixekizumab. This analysis demonstrates that MAIC methods can provide valid relative treatment effect estimates when direct comparisons are lacking, particularly when trials with similar designs and patient populations inform the analysis