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Articles 31 - 38 of 38
Full-Text Articles in Biostatistics
Composite Likelihood Bayesian Information Criteria For Model Selection In High Dimensional Data, X Gao, Peter Xuekun Song
Composite Likelihood Bayesian Information Criteria For Model Selection In High Dimensional Data, X Gao, Peter Xuekun Song
The University of Michigan Department of Biostatistics Working Paper Series
For high-dimensional data set with complicated dependency structures, the full likelihood approach often renders to intractable computational complexity. This imposes di±culty on model selection as most of the traditionally used information criteria require the evaluation of the full likelihood. We propose a composite likelihood version of the Bayesian information criterion (BIC) and establish its consistency property for the selection of the true underlying model. Under some mild regularity conditions, the proposed BIC is shown to be selection consistent, where the number of potential model parameters is allowed to increase to in¯nity at a certain rate of the sample size. Simulation …
Longitudinal Image Analysis Of Tumor/Brain Change In Contrast Uptake Induced By Radiation, Xiaoxi Zhang, Tim Johnson, Rod Little, Yue Cao
Longitudinal Image Analysis Of Tumor/Brain Change In Contrast Uptake Induced By Radiation, Xiaoxi Zhang, Tim Johnson, Rod Little, Yue Cao
The University of Michigan Department of Biostatistics Working Paper Series
This work is motivated by a quantitative Magnetic Resonance Imaging study of the differential tumor/healthy tissue change in contrast uptake induced by radiation. The goal is to determine the time in which there is maximal contrast uptake, a surrogate for permeability, in the tumor relative to healthy tissue. A notable feature of the data is its spatial heterogeneity. Zhang, Johnson, Little, and Cao (2008a and 2008b) discuss two parallel approaches to “denoise” a single image of change in contrast uptake from baseline to a single follow-up visit of interest. In this work we explore the longitudinal profile of the tumor/healthy …
Joint Multiple Testing Procedures For Graphical Model Selection With Applications To Biological Networks, Houston N. Gilbert, Mark J. Van Der Laan, Sandrine Dudoit
Joint Multiple Testing Procedures For Graphical Model Selection With Applications To Biological Networks, Houston N. Gilbert, Mark J. Van Der Laan, Sandrine Dudoit
U.C. Berkeley Division of Biostatistics Working Paper Series
Gaussian graphical models have become popular tools for identifying relationships between genes when analyzing microarray expression data. In the classical undirected Gaussian graphical model setting, conditional independence relationships can be inferred from partial correlations obtained from the concentration matrix (= inverse covariance matrix) when the sample size n exceeds the number of parameters p which need to estimated. In situations where n < p, another approach to graphical model estimation may rely on calculating unconditional (zero-order) and first-order partial correlations. In these settings, the goal is to identify a lower-order conditional independence graph, sometimes referred to as a ‘0-1 graphs’. For either choice of graph, model selection may involve a multiple testing problem, in which edges in a graph are drawn only after rejecting hypotheses involving (saturated or lower-order) partial correlation parameters. Most multiple testing procedures applied in previously proposed graphical model selection algorithms rely on standard, marginal testing methods which do not take into account the joint distribution of the test statistics derived from (partial) correlations. We propose and implement a multiple testing framework useful when testing for edge inclusion during graphical model selection. Two features of our methodology include (i) a computationally efficient and asymptotically valid test statistics joint null distribution derived from influence curves for correlation-based parameters, and (ii) the application of empirical Bayes joint multiple testing procedures which can effectively control a variety of popular Type I error rates by incorpo- rating joint null distributions such as those described here (Dudoit and van der Laan, 2008). Using a dataset from Arabidopsis thaliana, we observe that the use of more sophisticated, modular approaches to multiple testing allows one to identify greater numbers of edges when approximating an undirected graphical model using a 0-1 graph. Our framework may also be extended to edge testing algorithms for other types of graphical models (e.g., for classical undirected, bidirected, and directed acyclic graphs).
Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei
Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semiparametric Two-Part Models With Proportionality Constraints: Analysis Of The Multi-Ethnic Study Of Atherosclerosis (Mesa), Anna Liu, Richard Kronmal, Xiao-Hua Zhou, Shuangge Ma
Semiparametric Two-Part Models With Proportionality Constraints: Analysis Of The Multi-Ethnic Study Of Atherosclerosis (Mesa), Anna Liu, Richard Kronmal, Xiao-Hua Zhou, Shuangge Ma
UW Biostatistics Working Paper Series
SUMMARY. In this article, we analyze the coronary artery calcium (CAC) score in the Multi-Ethnic Study of Atherosclerosis (MESA), where about half of the CAC scores are zero and the rest are continuously distributed. When the observed data has a mixture distribution, two-part models can be the natural choice. With a two-part model, there are two covariate effects, with one in each part of the model. Determination of whether the two covariate effects are proportional can provide more insights into the process underlying development and progression of CAC. In this study, we model the CAC score using a semiparametric two-part …
Pooled Nucleic Acid Testing To Identify Antiretroviral Treatment Failure During Hiv Infection, Susanne May, Anthony Gamst, Richard Haubrich, Constance Benson, Davey Smith
Pooled Nucleic Acid Testing To Identify Antiretroviral Treatment Failure During Hiv Infection, Susanne May, Anthony Gamst, Richard Haubrich, Constance Benson, Davey Smith
UW Biostatistics Working Paper Series
Abstract Background: Pooling strategies have been used to reduce the costs of polymerase chain reaction based screening for acute HIV infection in populations where the prevalence of acute infection is low (<1%). Only limited research has been done for conditions where the prevalence of screening positivity is higher (>1%). Methods and Results: We present data on a variety of pooling strategies that incorporate the use of PCR-based quantitative measures to monitor for virologic failure among HIV-infected patients receiving antiretroviral therapy. For a prevalence of virologic failure between 1% and 25%, we demonstrate relative efficiency and accuracy of various strategies. These results could be used to choose the best strategy based on the requirements of individual laboratory …
Variance-Mean Relationships To Analyze Large Survey Data With Application To Health Expenditure Data, Wenli Luo
Variance-Mean Relationships To Analyze Large Survey Data With Application To Health Expenditure Data, Wenli Luo
Legacy Theses & Dissertations (2009 - 2024)
A great deal of work has been done in cost analysis in the last several decades. However, relatively little has been done to learn how efficiently to address the relationship between the variance and mean of the response distribution and how this will affect the choice of an appropriate generalized linear model.
Complete Identification Of Permissible Sampling Rates For First-Order Sampling Of Multi-Band Bandpass Signals, Yan Wu, Daniel F. Linder
Complete Identification Of Permissible Sampling Rates For First-Order Sampling Of Multi-Band Bandpass Signals, Yan Wu, Daniel F. Linder
Biostatistics: Faculty Publications
The first-order sampling of multi-band bandpass signals with arbitrary band positions is considered in this paper. Gaps between the spectral sub-bands are utilized to achieve lower sampling rates than the Nyquist. The lowest possible sampling rate along with other permissible sampling rates is identified via a unique partition of the frequency axis. With the complete identification of all the permissible sampling rates, a necessary and sufficient sampling theorem for multi-band bandpass signals is presented in terms of a series of csinc-interpolators.