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https://www.readbyqxmd.com/read/30229867/on-assessing-binary-regression-models-based-on-ungrouped-data
#1
Chunling Lu, Yuhong Yang
Assessing a binary regression model based on ungrouped data is a commonly encountered but very challenging problem. Although tests, such as Hosmer-Lemeshow test and le Cessie-van Houwelingen test, have been devised and widely used in applications, they often have low power in detecting lack of fit and not much theoretical justification has been made on when they can work well. In this paper, we propose a new approach based on a cross validation voting system to address the problem. In addition to a theoretical guarantee that the probabilities of type I and II errors both converge to zero as the sample size increases for the new method under proper conditions, our simulation results demonstrate that it performs very well...
September 19, 2018: Biometrics
https://www.readbyqxmd.com/read/30203467/power-and-sample-size-for-dose-finding-studies-with-survival-endpoints-under-model-uncertainty
#2
Qiqi Deng, Xiaofei Bai, Dacheng Liu, Dooti Roy, Zhiliang Ying, Dan-Yu Lin
Multiple comparison procedures combined with modeling techniques (MCPMod) (Bretz et al., 2005) is an efficient and robust statistical methodology for the model based design and analysis of dose-finding studies with an unknown dose-response model. With this approach, multiple comparison methods are used to identify statistically significant contrasts corresponding to a set of candidate dose-response models, and the best model is then used to estimate the target dose. Power and sample size calculations for this methodology require knowledge of the covariance matrix for the estimators of the (placebo-adjusted) mean responses among the dose groups...
September 11, 2018: Biometrics
https://www.readbyqxmd.com/read/30203414/a-novel-bayesian-multiple-testing-approach-to-deregulated-mirna-discovery-harnessing-positional-clustering
#3
Noirrit Kiran Chandra, Richa Singh, Sourabh Bhattacharya
MicroRNAs (miRNAs) are small non-coding RNAs that function as regulators of gene expression. In recent years, there has been a tremendous interest among researchers to investigate the role of miRNAs in normal as well as in disease processes. To investigate the role of miRNAs in oral cancer, we analyse expression levels of miRNAs to identify miRNAs with statistically significant differential expression in cancer tissues. In this article, we propose a novel Bayesian hierarchical model of miRNA expression data...
September 11, 2018: Biometrics
https://www.readbyqxmd.com/read/30178494/regularized-latent-class-model-for-joint-analysis-of-high-dimensional-longitudinal-biomarkers-and-a-time-to-event-outcome
#4
Jiehuan Sun, Jose D Herazo-Maya, Philip L Molyneaux, Toby M Maher, Naftali Kaminski, Hongyu Zhao
Although many modeling approaches have been developed to jointly analyze longitudinal biomarkers and a time-to-event outcome, most of these methods can only handle one or a few biomarkers. In this article, we propose a novel joint latent class model to deal with high dimensional longitudinal biomarkers. Our model has three components: a class membership model, a survival submodel, and a longitudinal submodel. In our model, we assume that covariates can potentially affect biomarkers and class membership. We adopt a penalized likelihood approach to infer which covariates have random effects and/or fixed effects on biomarkers, and which covariates are informative for the latent classes...
September 3, 2018: Biometrics
https://www.readbyqxmd.com/read/30168593/a-hidden-markov-model-for-identifying-differentially-methylated-sites-in-bisulfite-sequencing-data
#5
Farhad Shokoohi, David A Stephens, Guillaume Bourque, Tomi Pastinen, Celia M T Greenwood, Aurélie Labbe
DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly...
August 31, 2018: Biometrics
https://www.readbyqxmd.com/read/30133696/on-doubly-robust-estimation-of-the-hazard-difference
#6
Oliver Dukes, Torben Martinussen, Eric J Tchetgen Tchetgen, Stijn Vansteelandt
The estimation of conditional treatment effects in an observational study with a survival outcome typically involves fitting a hazards regression model adjusted for a high-dimensional covariate. Standard estimation of the treatment effect is then not entirely satisfactory, as the misspecification of the effect of this covariate may induce a large bias. Such misspecification is a particular concern when inferring the hazard difference, because it is difficult to postulate additive hazards models that guarantee non-negative hazards over the entire observed covariate range...
August 22, 2018: Biometrics
https://www.readbyqxmd.com/read/30129091/registration-for-exponential-family-functional-data
#7
Julia Wrobel, Vadim Zipunnikov, Jennifer Schrack, Jeff Goldsmith
We introduce a novel method for separating amplitude and phase variability in exponential family functional data. Our method alternates between two steps: the first uses generalized functional principal components analysis to calculate template functions, and the second estimates smooth warping functions that map observed curves to templates. Existing approaches to registration have primarily focused on continuous functional observations, and the few approaches for discrete functional data require a pre-smoothing step; these methods are frequently computationally intensive...
August 21, 2018: Biometrics
https://www.readbyqxmd.com/read/30125947/bayesian-negative-binomial-mixture-regression-models-for-the-analysis-of-sequence-count-and-methylation-data
#8
Qiwei Li, Alberto Cassese, Michele Guindani, Marina Vannucci
In this article, we develop a Bayesian hierarchical mixture regression model for studying the association between a multivariate response, measured as counts on a set of features, and a set of covariates. We have available RNA-Seq and DNA methylation data measured on breast cancer patients at different stages of the disease. We account for the heterogeneity and over-dispersion of count data (here, RNA-Seq data) by considering a mixture of negative binomial distributions and incorporate the covariates (here, methylation data) into the model via a linear modeling construction on the mean components...
August 20, 2018: Biometrics
https://www.readbyqxmd.com/read/30096729/on-the-alternative-hypotheses-for-the-win-ratio
#9
Lu Mao
We extend the results of Luo et al. (, Biometrics 71, 139-145) regarding the alternative hypotheses for the win ratio from hazard orders to the upper quadrant stochastic order on the plane. This extension substantially widens the range of alternatives against which the win ratio is known to be consistent. It incorporates alternatives induced by simple and popular copula models that are left out by the characterization of Luo et al. (). We also discuss how our results may be generalized to win ratios in multivariate and stratified settings...
August 10, 2018: Biometrics
https://www.readbyqxmd.com/read/30096727/rejoinder-to-on-the-alternative-hypotheses-for-the-win-ratio
#10
Xiaodong Luo, Hong Tian, Surya Mohanty, Wei Yann Tsai
No abstract text is available yet for this article.
August 10, 2018: Biometrics
https://www.readbyqxmd.com/read/30081434/joint-skeleton-estimation-of-multiple-directed-acyclic-graphs-for-heterogeneous-population
#11
Jianyu Liu, Wei Sun, Yufeng Liu
The directed acyclic graph (DAG) is a powerful tool to model the interactions of high-dimensional variables. While estimating edge directions in a DAG often requires interventional data, one can estimate the skeleton of a DAG (i.e., an undirected graph formed by removing the direction of each edge in a DAG) using observational data. In real data analyses, the samples of the high-dimensional variables may be collected from a mixture of multiple populations. Each population has its own DAG while the DAGs across populations may have significant overlap...
August 6, 2018: Biometrics
https://www.readbyqxmd.com/read/30081432/bayesian-additive-adaptive-basis-tensor-product-models-for-modeling-high-dimensional-surfaces-an-application-to-high-throughput-toxicity-testing
#12
Matthew W Wheeler
Many modern datasets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective and well suited for characterizing a surface in two or three dimensions, but they may suffer from difficulties when representing higher dimensional surfaces. Motivated by high throughput toxicity testing where observed dose-response curves are cross sections of a surface defined by a chemical's structural properties, a model is developed to characterize this surface to predict untested chemicals' dose-responses...
August 6, 2018: Biometrics
https://www.readbyqxmd.com/read/30079539/removal-models-accounting-for-temporary-emigration
#13
Ming Zhou, Rachel S McCrea, Eleni Matechou, Diana J Cole, Richard A Griffiths
Removal of protected species from sites scheduled for development is often a legal requirement in order to minimize the loss of biodiversity. The assumption of closure in the classic removal model will be violated if individuals become temporarily undetectable, a phenomenon commonly exhibited by reptiles and amphibians. Temporary emigration can be modeled using a multievent framework with a partial hidden process, where the underlying state process describes the movement pattern of animals between the survey area and an area outside of the study...
August 6, 2018: Biometrics
https://www.readbyqxmd.com/read/30076713/flexible-parametric-approach-to-classical-measurement-error-variance-estimation-without-auxiliary-data
#14
Aurélie Bertrand, Ingrid Van Keilegom, Catherine Legrand
Measurement error in the continuous covariates of a model generally yields bias in the estimators. It is a frequent problem in practice, and many correction procedures have been developed for different classes of models. However, in most cases, some information about the measurement error distribution is required. When neither validation nor auxiliary data (e.g., replicated measurements) are available, this specification turns out to be tricky. In this article, we develop a flexible likelihood-based procedure to estimate the variance of classical additive error of Gaussian distribution, without additional information, when the covariate has compact support...
August 4, 2018: Biometrics
https://www.readbyqxmd.com/read/30073669/instrumental-variable-estimation-in-semi-parametric-additive-hazards-models
#15
Matthias Brueckner, Andrew Titman, Thomas Jaki
Instrumental variable methods allow unbiased estimation in the presence of unmeasured confounders when an appropriate instrumental variable is available. Two-stage least-squares and residual inclusion methods have recently been adapted to additive hazard models for censored survival data. The semi-parametric additive hazard model which can include time-independent and time-dependent covariate effects is particularly suited for the two-stage residual inclusion method, since it allows direct estimation of time-independent covariate effects without restricting the effect of the residual on the hazard...
August 2, 2018: Biometrics
https://www.readbyqxmd.com/read/30052272/sufficient-dimension-reduction-via-random-partitions-for-the-large-p-small-n-problem
#16
Hung Hung, Su-Yun Huang
Sufficient dimension reduction (SDR) continues to be an active field of research. When estimating the central subspace (CS), inverse regression based SDR methods involve solving a generalized eigenvalue problem, which can be problematic under the large-p-small-n situation. In recent years, new techniques have emerged in numerical linear algebra, called randomized algorithms or random sketching, for high-dimensional and large scale problems. To overcome the large-p-small-n SDR problem, we combine the idea of statistical inference with random sketching to propose a new SDR method, called integrated random-partition SDR (iRP-SDR)...
July 27, 2018: Biometrics
https://www.readbyqxmd.com/read/30051914/bayesian-inference-of-hub-nodes-across-multiple-networks
#17
Junghi Kim, Kim-Anh Do, Min Jin Ha, Christine B Peterson
Hub nodes within biological networks play a pivotal role in determining phenotypes and disease outcomes. In the multiple network setting, we are interested in understanding network similarities and differences across different experimental conditions or subtypes of disease. The majority of proposed approaches for joint modeling of multiple networks focus on the sharing of edges across graphs. Rather than assuming the network similarities are driven by individual edges, we instead focus on the presence of common hub nodes, which are more likely to be preserved across settings...
July 27, 2018: Biometrics
https://www.readbyqxmd.com/read/30051909/information-content-of-cluster-period-cells-in-stepped-wedge-trials
#18
Jessica Kasza, Andrew B Forbes
Stepped wedge and other multiple-period cluster randomized trials, which collect data from multiple clusters across multiple time periods, are being conducted with increasing frequency; statistical research into these designs has not kept apace. In particular, some stepped wedge designs with missing cluster-period "cells" have been proposed without any formal justification. Indeed there are no general guidelines regarding which cells of a stepped wedge design contribute the least information toward estimation of the treatment effect, and correspondingly which may be preferentially omitted...
July 27, 2018: Biometrics
https://www.readbyqxmd.com/read/30039859/generalized-linear-models-with-linear-constraints-for-microbiome-compositional-data
#19
Jiarui Lu, Pixu Shi, Hongzhe Li
Motivated by regression analysis for microbiome compositional data, this article considers generalized linear regression analysis with compositional covariates, where a group of linear constraints on regression coefficients are imposed to account for the compositional nature of the data and to achieve subcompositional coherence. A penalized likelihood estimation procedure using a generalized accelerated proximal gradient method is developed to efficiently estimate the regression coefficients. A de-biased procedure is developed to obtain asymptotically unbiased and normally distributed estimates, which leads to valid confidence intervals of the regression coefficients...
July 24, 2018: Biometrics
https://www.readbyqxmd.com/read/30039855/logistic-regression-augmented-community-detection-for-network-data-with-application-in-identifying-autism-related-gene-pathways
#20
Yunpeng Zhao, Qing Pan, Chengan Du
When searching for gene pathways leading to specific disease outcomes, additional information on gene characteristics is often available that may facilitate to differentiate genes related to the disease from irrelevant background when connections involving both types of genes are observed and their relationships to the disease are unknown. We propose method to single out irrelevant background genes with the help of auxiliary information through a logistic regression, and cluster relevant genes into cohesive groups using the adjacency matrix...
July 24, 2018: Biometrics
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