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arXiv 2022年

作者： Park, Jina Hu, Wenjing Jin, Ick Hoon Liu, Hao Zang, Yong Department of Applied Statistics Yonsei University Korea Republic of Department of Statistics and Data Science Yonsei University Korea Republic of AT&T Company United States Department of Biostatistics and Epidemiology Cancer Institute of New Jersey Rutgers University United States Department of Biostatistics and Health Data Sciences Center of Computational Biology and Bioinformatics Indiana University United States

Many phase II clinical trials have used survival outcomes as the primary endpoints in recent decades. Suppose the radiotherapy is evaluated in a phase II trial using survival outcomes. In that case, the competing risk issue often arises because the time to disease progression can be censored by the time to normal tissue complications, and vice versa. Besides, much literature has examined that patients receiving the same radiotherapy dose may yield distinct responses due to their heterogeneous radiation susceptibility statuses. Therefore, the "one-dose-fit-all" strategy often fails, and it is more relevant to evaluate the subgroup-specific treatment effect with the subgroup defined by the radiation susceptibility status. In this paper, we propose a Bayesian precision phase II trial design evaluating the subgroup-specific treatment effects of radiotherapy. We use the cause-specific hazard approach to model the competing risk survival outcomes. We propose restricting the candidate radiation doses based on each patient’s radiation susceptibility status. Only the clinically feasible personalized dose will be considered, which enhances the benefit for the patients in the trial. In addition, we propose a stratified Bayesian adaptive randomization scheme such that more patients will be randomized to the dose reporting more favorable survival outcomes. Numerical studies have shown that the proposed design performed well and outperformed the conventional design ignoring the competing risk issue. Copyright © 2022, The Authors. All rights reserved.

关键词： Random processes

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Profile and globe tests of mean surfaces for two-sample bivariate functional data

arXiv

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arXiv 2019年

作者： Yang, Jin Zhang, Tao Liu, Chunling Yuen, Kam Chuen Liu, Aiyi School of Mathematics and Statistics Nankai University Tianjin300071 China School of Sciences Guangxi University of Science and Technology Liuzhou545006 China Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong Department of Statistics and Actuarial Science University of Hong Kong Hong Kong Biostatistics and Bioinformatics Branch NICHD NIH BethesdaMD United States

Multivariate functional data has received considerable attention but testing for equality of mean surfaces and its profile has limited progress. The existing literature has tested equality of either mean curves of univariate functional samples directly, or mean surfaces of bivariate functional data samples but turn into functional curves comparison again. In this paper, we aim to develop both the profile and globe tests of mean surfaces for two-sample bivariate functional data. We present valid approaches of tests by employing the idea of pooled projection and by developing a novel profile functional principal component analysis tool. The proposed methodology enjoys the merit of readily interpretability and implementation. Under mild conditions, we de- rive the asymptotic behaviors of test statistics under null and alternative hypotheses. Simulations show that the proposed tests have a good control of the type I error by the size and can detect difference in mean surfaces and its profile effectively in terms of power in finite samples. Finally, we apply the testing procedures to two real data sets associated with the precipitation change affected jointly by time and locations in the Midwest of USA, and the trends in human mortality from European period life tables. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistical tests

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Functional Clustering for Longitudinal Associations between Social Determinants of Health and Stroke Mortality in the US

arXiv

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arXiv 2024年

作者： Luo, Fangzhi Tan, Jianbin Zhang, Donglan Huang, Hui Shen, Ye Department of Epidemiology and Biostatistics College of Public Health University of Georgia AthensGA United States Department of Biostatistics and Bioinformatics School of Medicine Duke University DurhamNC United States Department of Foundations of Medicine NYU Grossman Long Island School of Medicine MineolaNY United States Center for Applied Statistics School of Statistics Renmin University of China Beijing China

Understanding the longitudinally changing associations between Social Determinants of Health (SDOH) and stroke mortality is essential for effective stroke management. Previous studies have uncovered significant regional disparities in the associations between SDOH and stroke mortality. However, existing studies have not utilized longitudinal associations to develop data-driven methods for regional division in stroke control. To fill this gap, we propose a novel clustering method to analyze SDOH-stroke mortality associations across US counties. To enhance the interpretability of the clustering outcomes, we introduce a novel regularized expectation-maximization algorithm equipped with sparsity- and smoothness-pursued penalties, aiming at simultaneous clustering and variable selection in longitudinal associations. As a result, we can identify key SDOH that contributes to longitudinal changes in stroke mortality. This facilitates the clustering of US counties based on the associations between these SDOH and stroke mortality. The effectiveness of our proposed method is demonstrated through extensive numerical studies. By applying our method to longitudinal data on SDOH and stroke mortality at the county level, we identify 18 important SDOH for stroke mortality and divide the US counties into two clusters based on these selected SDOH. Our findings unveil complex regional heterogeneity in the longitudinal associations between SDOH and stroke mortality, providing valuable insights into region-specific SDOH adjustments for mitigating stroke mortality. © 2024, CC BY-NC-ND.

关键词： Expectation maximization algorithm

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The theory of prioritized Muirhead mean operators under the presence of Complex Single-Valued Neutrosophic values

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Decision Analytics Journal 2023年 7卷

作者： Imran, Raiha Ullah, Kifayat Ali, Zeeshan Akram, Maria Senapati, Tapan Department of Mathematics Riphah Institute of Computing & Applied Sciences (RICAS) Riphah International University (Lahore Campus) Lahore 54000 Pakistan Department of Mathematics & Statistics International Islamic University Islamabad Islamabad 44000 Pakistan KERMIT Department of Mathematical Modeling Statistics and Bioinformatics Coupure Links 653 Ghent University Ghent Belgium School of Mathematics and Statistics Southwest University Chongqing Beibei 400715 China

The Muirhead mean (MM) operator is effective and feasible and is used for aggregating the finite collection of attributes into a singleton set, where the averaging and geometric aggregation operators are the special cases of the MM operator. Furthermore, the Complex Single-Valued Neutrosophic (CSVN) set theory is more reliable than the intuitionistic fuzzy set theory because it can easily handle awkward and unreliable information with the triplet, such as yes, abstinence, and no. In this manuscript, we present the theory of MM operators under the presence of CSVN values, such as the CSVN prioritized MM (CSVNPMM) operator and the CSVN prioritized dual MM (CSVNPDMM) operator. Some important and dominant properties are examined in the derived theory. Additionally, to verify the above information with the help of some appropriate examples, we demonstrate a Multi-Attribute Decision-Making (MADM) method for the proposed operators under the consideration of CSVN information. Finally, we compare the derived operators with some prevailing information to state the practicality and validity of the proposed method. © 2023 The Author(s)

关键词： Complex single-valued neutrosophic sets Decision support Dual Muirhead mean operators Multi-attribute decision-making Prioritized Muirhead

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Discrimination of attractors with noisy nodes in boolean networks

arXiv

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arXiv 2020年

作者： Cheng, Xiaoqing Ching, Wai-Ki Guo, Sini Akutsu, Tatsuya School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China Advanced Modelling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Bioinformatics Center Institute for Chemical Research Kyoto University Gokasho UjiKyoto611-0011 Japan

Observing the internal state of the whole system using a small number of sensor nodes is important in analysis of complex networks. Here, we study the problem of determining the minimum number of sensor nodes to discriminate attractors under the assumption that each attractor has at most K noisy nodes. We present exact and approximation algorithms for this minimization problem. The effectiveness of the algorithms is also demonstrated by computational experiments using both synthetic data and realistic biological data. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamical systems

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Probabilistic partial least squares model: Identifiability, estimation and application

arXiv

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arXiv 2017年

作者： el Bouhaddani, Said Uh, Hae-Won Hayward, Caroline Jongbloed, Geurt Houwing-Duistermaat, Jeanine Department of Medical statistics and bioinformatics Leiden University Medical Center Netherlands Department of Biostatistics and Research Support UMC Utrecht div. Julius Centrum University Medical Center Utrecht Netherlands Department of Applied Mathematics Delft University of Technology Netherlands Department of Statistics University of Leeds United Kingdom MRC Human Genetics Unit Institute of Genetics and Molecular Medicine University of Edinburgh United Kingdom

With a rapid increase in volume and complexity of data sets, there is a need for methods that can extract useful information, for example the relationship between two data sets measured for the same persons. The Partial Least Squares (PLS) method can be used for this dimension reduction task. Within life sciences, results across studies are compared and combined. Therefore, parameters need to be identifiable, which is not the case for PLS. In addition, PLS is an algorithm, while epidemiological study designs are often outcome-dependent and methods to analyze such data require a probabilistic formulation. Moreover, a probabilistic model provides a statistical framework for inference. To address these issues, we develop Probabilistic PLS (PPLS). We derive maximum likelihood estimators that satisfy the identifiability conditions by using an EM algorithm with a constrained optimization in the M step. We show that the PPLS parameters are identifiable up to sign. A simulation study is conducted to study the performance of PPLS compared to existing methods. The PPLS estimates performed well in various scenarios, even in high dimensions. Most notably, the estimates seem to be robust against departures from normality. To illustrate our method, we applied it to IgG glycan data from two cohorts. Our PPLS model provided insight as well as interpretable results across the two cohorts. Copyright © 2017, The Authors. All rights reserved.

关键词： Least squares approximations

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Median polish with power transformations as an alternative for the analysis of contingency tables with patient data

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1st International Conference on Health Information Science, HIS 2012

作者： Klawonn, Frank Crull, Katja Kukita, Akiko Pessler, Frank Department of Computer Science Ostfalia University of Applied Sciences Salzdahlumer Str. 46/48 D-38302 Wolfenbuettel Germany Bioinformatics and Statistics Helmholtz Centre for Infection Research Inhoffenstr. 7 D-38124 Braunschweig Germany Department of Molecular Immunology Helmholtz Centre for Infection Research Inhoffenstr. 7 D-38124 Braunschweig Germany Department of Microbiology Saga Medical School Saga Japan Department of Infection Genetics Helmholtz Centre for Infection Research Inhoffenstr. 7 D-38124 Braunschweig Germany

ISBN: (纸本)9783642293603

Contingency tables are a very common basis for the investigation of effects of different treatments or influences on a disease or the health state of patients. Many journals put a strong emphasis on p-values to support the validity of results. Therefore, even small contingency tables are analysed by techniques like t-test or ANOVA. Both these concepts are based on normality assumptions for the underlying data. For larger data sets, this assumption is not so critical, since the underlying statistics are based on sums of (independent) random variables which can be assumed to follow approximately a normal distribution, at least for a larger number of summands. But for smaller data sets, the normality assumption can often not be justified. Robust methods like the Wilcoxon-Mann-Whitney-U test or the Kruskal-Wallis test do not lead to statistically significant p-values for small samples. Median polish is a robust alternative to analyse contingency tables providing much more insight than just a p-value. In this paper we discuss different ways to apply median polish to contingency tables in the context of medical data and how to interpret the results based on different examples. We also introduce a technique based on power transformations to find a suitable transformation of the data before applying median polish. © 2012 Springer-Verlag.

关键词： Normal distribution

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Feature selection using stochastic gates

arXiv

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arXiv 2018年

作者： Yamada, Yutaro Lindenbaum, Ofir Negahban, Sahand Kluger, Yuval Statistics Department Yale University New HavenCT United States Applied Mathematics Program Yale University New HavenCT United States Computational Biology and Bioinformatics Yale University New HavenCT United States Department of Pathology Yale University New HavenCT United States

Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in high-dimensional non-linear function estimation problems. The new procedure is based on minimizing the `0 norm of the vector of indicator variables that represent if a feature is selected or not. Our approach relies on the continuous relaxation of Bernoulli distributions, which allows our model to learn the parameters of the approximate Bernoulli distributions via gradient descent. This general framework simultaneously minimizes a loss function while selecting relevant features. Furthermore, we provide an information-theoretic justification of incorporating Bernoulli distribution into our approach and demonstrate the potential of the approach on synthetic and real-life applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Feature extraction

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Mosim: Multi-Omics Simulation in R

SSRN

引用

SSRN 2023年

作者： Tarazona, Sonia Martínez-Mira, Carlos Conesa, Ana Biobam Bioinformatics S.L. València Spain Paterna Spain Microbiology and Cell Science Department Institute for Food and Agricultural Research University of Florida Gainesville United States Applied Statistics Operational Research and Quality Department Universitat Politècnica de València València Spain

Motivation: As new integrative methodologies are being developed to analyse multi-omicexperiments and derive multi-layer regulatory networks, validation strategies are required forbenchmarking. In silico approaches such as simulated data are popular for tuning, validatingand testing analytical methods as they are fast and cheap. However, few tools are available forcreating synthetic multi-omic datasets with flexible experimental designs: several experimentalgroups, replicates, time series, ***: MOSim is a new R Bioconductor package for easily simulating multi-omic sequencingexperiments consisting of gene expression data;other regulatory omics such as methylation,chromatin accessibility, microRNA expression or transcription factor binding and expression;and their regulatory relationships with gene expression. MOSim supports different experimentaldesigns including time series data and differential expression between experimental conditionsor across time. MOSim returns a count matrix for each simulated omic (probability matrix in thecase of methylation), the list of differentially expressed features for each of them and the activeregulatory relationships between regulatory omics and gene expression data. In brief, the MOSimR package is a flexible and useful tool to generate multi-omic datasets that are essential to tune,validate and test multi-omic integration ***: MOSim is available as an R Bioconductor package athttps://***/packages/release/bioc/html/***. © 2023, The Authors. All rights reserved.

关键词： Methylation

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A Flow Artist for High-Dimensional Cellular Data

A Flow Artist for High-Dimensional Cellular Data

引用

IEEE Workshop on Machine Learning for Signal Processing

作者： Kincaid MacDonald Dhananjay Bhaskar Guy Thampakkul Nhi Nguyen Joia Zhang Michael Perlmutter Ian Adelstein Smita Krishnaswamy Department of Mathematics Yale University Department of Genetics Yale School of Medicine Department of Computer Science Yale University Department of Mathematics Pomona College Applied Mathematics Program Yale University Department of Statistics University of Washington Department of Mathematics Boise State University Computational Biology and Bioinformatics Program Yale University

We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured, including in high-throughput biology such as single-cell transcriptomics. Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i.e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field. Here we present FlowArtist, a neural network that embeds points while jointly learning a vector field around the points. The combination allows FlowArtist to better separate and visualize velocity-informed structures. Our results, on toy datasets and single-cell RNA velocity data, illustrate the value of utilizing coordinate and velocity information in tandem for embedding and visualizing high-dimensional data.

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