Multivariate linear rank statistics for profile analysis

For some general multivariate linear models, linear rank statistics are used in conjunction with Roy's Union-Intersection Principle to develop some tests for inference on the parameter (vector) when they are subject to certain linear constraints. More powerful tests are designed by incorporating the a priori information on these constraints. Profile analysis is an important application of this type of hypothesis testing problem; it consists of a set of hypothesis testing problem for the p responses q-sample model, where it is a priori assumed that the response-sample interactions are null

Deep Latent Variable Model for Longitudinal Group Factor Analysis

In many scientific problems such as video surveillance, modern genomic analysis, and clinical studies, data are often collected from diverse domains across time that exhibit time-dependent heterogeneous properties. It is important to not only integrate data from multiple sources (called multiview data), but also to incorporate time dependency for deep understanding of the underlying system. Latent factor models are popular tools for exploring multi-view data. However, it is frequently observed that these models do not perform well for complex systems and they are not applicable to time-series data. Therefore, we propose a generative model based on variational autoencoder and recurrent neural network to infer the latent dynamic factors for multivariate timeseries data. This approach allows us to identify the disentangled latent embeddings across multiple modalities while accounting for the time factor. We invoke our proposed model for analyzing three datasets on which we demonstrate the effectiveness and the interpretability of the model

Probabilistic Model Incorporating Auxiliary Covariates to Control FDR

Controlling False Discovery Rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods rely on the test-level covariates while ignoring metrics about test-level covariates. This strategy may not be optimal for complex large-scale problems, where indirect relations often exist among test-level covariates and auxiliary metrics or covariates. We incorporate auxiliary covariates among test-level covariates in a deep Black-Box framework controlling FDR (named as NeurT-FDR) which boosts statistical power and controls FDR for multiple-hypothesis testing. Our method parametrizes the test-level covariates as a neural network and adjusts the auxiliary covariates through a regression framework, which enables flexible handling of high-dimensional features as well as efficient end-to-end optimization. We show that NeurT-FDR makes substantially more discoveries in three real datasets compared to competitive baselines.Comment: Short Version of NeurT-FDR, accepted at CIKM 2022. arXiv admin note: substantial text overlap with arXiv:2101.0980

A Bayesian Secondary Analysis in an Asthma Study

A recent study published in the New England Journal of Medicine by the Asthma Clinical Research Network (ACRN) compared three different treatments for their effectiveness in treating adults with uncontrolled asthma. This paper will describe the study design and its results, then detail the beginnings of a secondary analysis using Bayesian methods to estimate the parameters of interest. The methods will be explained, and the preliminary estimates given and contextualized. The paper will conclude with a discussion of the next steps and the goals for further analysis of the data in this study

Robustness and monotonicity properties of generalized correlation coefficients

[[abstract]]A new class of generalized correlation coefficients that contains the Pearson and Kendall statistics as special cases was defined by Chinchilli et al. (2005) and applied to the estimation of correlations coefficients within the context of 2×2 cross-over designs for clinical trials. In this paper, we determine the infinitesimal robustness and local stability properties of these generalized correlation coefficients by deriving their corresponding influence functions. For cases in which the population distribution is a bivariate normal or a mixture of bivariate normal distributions we obtain explicit formulas, and establish monotonicity and sign-reverse rule properties of the generalized correlation coefficients.[[journaltype]]國外[[booktype]]紙本[[countrycodes]]NL

Effect of the Tyrosine Kinase Inhibitors (Sunitinib, Sorafenib, Dasatinib, and Imatinib) on Blood Glucose Levels in Diabetic and Non-diabetic Patients in General Clinical Practice (Poster)

Tyrosine kinase is a key enzyme activity utilized in many intracellular messaging pathways. Understanding the role of particular tyrosine kinases in malignancies has allowed for the design of tyrosine kinase inhibitors (TKIs), which can target these enzymes and interfere with downstream signaling. TKIs have proven to be successful in the treatment of chronic myeloid leukemia, renal cell carcinoma and gastrointestinal stromal tumor, and other malignancies. Scattered reports have suggested that these agents appear to affect blood glucose (BG). We retrospectively studied the BG concentrations in diabetic (17) and nondiabetic (61) patients treated with dasatinib (8), imatinib (39), sorafenib (23), and sunitinib (30) in our clinical practice. Mean declines of BG were dasatinib (53 mg/dL), imatinib (9 mg/dL), sorafenib (12 mg/dL), and sunitinib (14 mg/dL). All these declines in BG were statistically significant. Of note, 47% (8/17) of the patients with diabetes were able to discontinue their medications, including insulin in some patients. Only one diabetic patient developed symptomatic hypoglycemia while on sunitinib. The mechanism for the hypoglycemic effect of these drugs is unclear, but of the four agents tested, c-kit and PDGFRβ are the common target kinases. Clinicians should keep the potential hypoglycemic effects of these agents in mind; modification of hypoglycemic agents may be required in diabetic patients. These results also suggest that inhibition of a tyrosine kinase, be it c-kit, PDGFRβ or some other undefined target, may improve diabetes mellitus BG control and it deserves further study as a potential novel therapeutic option

A Murine Model to Study Epilepsy and SUDEP Induced by Malaria Infection.

One of the largest single sources of epilepsy in the world is produced as a neurological sequela in survivors of cerebral malaria. Nevertheless, the pathophysiological mechanisms of such epileptogenesis remain unknown and no adjunctive therapy during cerebral malaria has been shown to reduce the rate of subsequent epilepsy. There is no existing animal model of postmalarial epilepsy. In this technical report we demonstrate the first such animal models. These models were created from multiple mouse and parasite strain combinations, so that the epilepsy observed retained universality with respect to genetic background. We also discovered spontaneous sudden unexpected death in epilepsy (SUDEP) in two of our strain combinations. These models offer a platform to enable new preclinical research into mechanisms and prevention of epilepsy and SUDEP