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Articles 31 - 60 of 178
Full-Text Articles in Physical Sciences and Mathematics
Adaptive Randomized Trial Designs That Cannot Be Dominated By Any Standard Design At The Same Total Sample Size, Michael Rosenblum
Adaptive Randomized Trial Designs That Cannot Be Dominated By Any Standard Design At The Same Total Sample Size, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
Prior work has shown that certain types of adaptive designs can always be dominated by a suitably chosen, standard, group sequential design. This applies to adaptive designs with rules for modifying the total sample size. A natural question is whether analogous results hold for other types of adaptive designs. We focus on adaptive enrichment designs, which involve preplanned rules for modifying enrollment criteria based on accrued data in a randomized trial. Such designs often involve multiple hypotheses, e.g., one for the total population and one for a predefined subpopulation, such as those with high disease severity at baseline. We fix …
Estimating Population Treatment Effects From A Survey Sub-Sample, Kara E. Rudolph, Ivan Diaz, Michael Rosenblum, Elizabeth A. Stuart
Estimating Population Treatment Effects From A Survey Sub-Sample, Kara E. Rudolph, Ivan Diaz, Michael Rosenblum, Elizabeth A. Stuart
Johns Hopkins University, Dept. of Biostatistics Working Papers
We consider the problem of estimating an average treatment effect for a target population from a survey sub-sample. Our motivating example is generalizing a treatment effect estimated in a sub-sample of the National Comorbidity Survey Replication Adolescent Supplement to the population of U.S. adolescents. To address this problem, we evaluate easy-to-implement methods that account for both non-random treatment assignment and a non-random two-stage selection mechanism. We compare the performance of a Horvitz-Thompson estimator using inverse probability weighting (IPW) and two double robust estimators in a variety of scenarios. We demonstrate that the two double robust estimators generally outperform IPW in …
Joint Estimation Of Multiple Graphical Models From High Dimensional Time Series, Huitong Qiu, Fang Han, Han Liu, Brian Caffo
Joint Estimation Of Multiple Graphical Models From High Dimensional Time Series, Huitong Qiu, Fang Han, Han Liu, Brian Caffo
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this manuscript the problem of jointly estimating multiple graphical models in high dimensions is considered. It is assumed that the data are collected from n subjects, each of which consists of m non-independent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of the closeness between subjects. A kernel based method for jointly estimating all graphical models is proposed. Theoretically, under a double asymptotic framework, where both (m,n) and the dimension d can increase, the explicit rate of convergence in parameter estimation is provided, thus characterizing the strength one can borrow …
Sparse Median Graphs Estimation In A High Dimensional Semiparametric Model, Fang Han, Han Liu, Brian Caffo
Sparse Median Graphs Estimation In A High Dimensional Semiparametric Model, Fang Han, Han Liu, Brian Caffo
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this manuscript a unified framework for conducting inference on complex aggregated data in high dimensional settings is proposed. The data are assumed to be a collection of multiple non-Gaussian realizations with underlying undirected graphical structures. Utilizing the concept of median graphs in summarizing the commonality across these graphical structures, a novel semiparametric approach to modeling such complex aggregated data is provided along with robust estimation of the median graph, which is assumed to be sparse. The estimator is proved to be consistent in graph recovery and an upper bound on the rate of convergence is given. Experiments on both …
Fast Covariance Estimation For High-Dimensional Functional Data, Luo Xiao, David Ruppert, Vadim Zipunnikov, Ciprian Crainiceanu
Fast Covariance Estimation For High-Dimensional Functional Data, Luo Xiao, David Ruppert, Vadim Zipunnikov, Ciprian Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
For smoothing covariance functions, we propose two fast algorithms that scale linearly with the number of observations per function. Most available methods and software cannot smooth covariance matrices of dimension J x J with J>500; the recently introduced sandwich smoother is an exception, but it is not adapted to smooth covariance matrices of large dimensions such as J \ge 10,000. Covariance matrices of order J=10,000, and even J=100,000$ are becoming increasingly common, e.g., in 2- and 3-dimensional medical imaging and high-density wearable sensor data. We introduce two new algorithms that can handle very large covariance matrices: 1) FACE: a …
Uniformly Most Powerful Tests For Simultaneously Detecting A Treatment Effect In The Overall Population And At Least One Subpopulation, Michael Rosenblum
Uniformly Most Powerful Tests For Simultaneously Detecting A Treatment Effect In The Overall Population And At Least One Subpopulation, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
After conducting a randomized trial, it is often of interest to determine treatment effects in the overall study population, as well as in certain subpopulations. These subpopulations could be defined by a risk factor or biomarker measured at baseline. We focus on situations where the overall population is partitioned into two predefined subpopulations. When the true average treatment effect for the overall population is positive, it logically follows that it must be positive for at least one subpopulation. We construct new multiple testing procedures that are uniformly most powerful for simultaneously rejecting the overall population null hypothesis and at least …
Soft Null Hypotheses: A Case Study Of Image Enhancement Detection In Brain Lesions, Haochang Shou, Russell T. Shinohara, Han Liu, Daniel Reich, Ciprian Crainiceanu
Soft Null Hypotheses: A Case Study Of Image Enhancement Detection In Brain Lesions, Haochang Shou, Russell T. Shinohara, Han Liu, Daniel Reich, Ciprian Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
This work is motivated by a study of a population of multiple sclerosis (MS) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify active brain lesions. At each visit, a contrast agent is administered intravenously to a subject and a series of images is acquired to reveal the location and activity of MS lesions within the brain. Our goal is to identify and quantify lesion enhancement location at the subject level and lesion enhancement patterns at the population level. With this example, we aim to address the difficult problem of transforming a qualitative scientific null hypothesis, such as "this …
Trial Designs That Simultaneously Optimize The Population Enrolled And The Treatment Allocation Probabilities, Brandon S. Luber, Michael Rosenblum, Antoine Chambaz
Trial Designs That Simultaneously Optimize The Population Enrolled And The Treatment Allocation Probabilities, Brandon S. Luber, Michael Rosenblum, Antoine Chambaz
Johns Hopkins University, Dept. of Biostatistics Working Papers
Standard randomized trials may have lower than desired power when the treatment effect is only strong in certain subpopulations. This may occur, for example, in populations with varying disease severities or when subpopulations carry distinct biomarkers and only those who are biomarker positive respond to treatment. To address such situations, we develop a new trial design that combines two types of preplanned rules for updating how the trial is conducted based on data accrued during the trial. The aim is a design with greater overall power and that can better determine subpopulation specific treatment effects, while maintaining strong control of …
Restricted Likelihood Ratio Tests For Functional Effects In The Functional Linear Model, Bruce J. Swihart, Jeff Goldsmith, Ciprian M. Crainiceanu
Restricted Likelihood Ratio Tests For Functional Effects In The Functional Linear Model, Bruce J. Swihart, Jeff Goldsmith, Ciprian M. Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
The goal of our article is to provide a transparent, robust, and computationally feasible statistical approach for testing in the context of scalar-on-function linear regression models. In particular, we are interested in testing for the necessity of functional effects against standard linear models. Our methods are motivated by and applied to a large longitudinal study involving diffusion tensor imaging of intracranial white matter tracts in a susceptible cohort. In the context of this study, we conduct hypothesis tests that are motivated by anatomical knowledge and which support recent findings regarding the relationship between cognitive impairment and white matter demyelination. R-code …
Optimal Tests Of Treatment Effects For The Overall Population And Two Subpopulations In Randomized Trials, Using Sparse Linear Programming, Michael Rosenblum, Han Liu, En-Hsu Yen
Optimal Tests Of Treatment Effects For The Overall Population And Two Subpopulations In Randomized Trials, Using Sparse Linear Programming, Michael Rosenblum, Han Liu, En-Hsu Yen
Johns Hopkins University, Dept. of Biostatistics Working Papers
We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such sub-populations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible to solve using existing techniques. Our solution involves a novel approach, in which we first transform the original multiple testing problem into a large, sparse linear …
Structured Functional Principal Component Analysis, Haochang Shou, Vadim Zipunnikov, Ciprian Crainiceanu, Sonja Greven
Structured Functional Principal Component Analysis, Haochang Shou, Vadim Zipunnikov, Ciprian Crainiceanu, Sonja Greven
Johns Hopkins University, Dept. of Biostatistics Working Papers
Motivated by modern observational studies, we introduce a class of functional models that expands nested and crossed designs. These models account for the natural inheritance of correlation structure from sampling design in studies where the fundamental sampling unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for ultra-high dimensional data. Methods are illustrated in three examples: high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity …
Penalized Function-On-Function Regression, Andrada E. Ivanescu, Ana-Maria Staicu, Fabian Scheipl, Sonja Greven
Penalized Function-On-Function Regression, Andrada E. Ivanescu, Ana-Maria Staicu, Fabian Scheipl, Sonja Greven
Johns Hopkins University, Dept. of Biostatistics Working Papers
We propose a general framework for smooth regression of a functional response on one or multiple functional predictors. Using the mixed model representation of penalized regression expands the scope of function on function regression to many realistic scenarios. In particular, the approach can accommodate a densely or sparsely sampled functional response as well as multiple functional predictors that are observed: 1) on the same or different domains than the functional response; 2) on a dense or sparse grid; and 3) with or without noise. It also allows for seamless integration of continuous or categorical covariates and provides approximate confidence intervals …
Predicting Human Movement Type Based On Multiple Accelerometers Using Movelets, Bing He, Jiawei Bai, Annemarie Koster, Casserotti Paolo, Nancy Glynn, Tamara B. Harris, Ciprian Crainiceanu
Predicting Human Movement Type Based On Multiple Accelerometers Using Movelets, Bing He, Jiawei Bai, Annemarie Koster, Casserotti Paolo, Nancy Glynn, Tamara B. Harris, Ciprian Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
We introduce statistical methods for prediction of types of human movement based on three tri-axial accelerometers worn simultaneously at the hip, left, and right wrist. We compare the individual performance of the three accelerometers using movelets and propose a new prediction algorithm that integrates the information from all three accelerometers. The development is motivated by a study of 20 older subjects who were instructed to perform 15 different types of activities during in-laboratory sessions. The differences in the prediction performance for different activity types among the three accelerometers reveal subtle yet important insights into how the intrinsic physical features of …
Likelihood Ratio Tests For The Mean Structure Of Correlated Functional Processes, Ana-Maria Staicu, Yingxing Li, Ciprian Crainiceanu, David M. Ruppert
Likelihood Ratio Tests For The Mean Structure Of Correlated Functional Processes, Ana-Maria Staicu, Yingxing Li, Ciprian Crainiceanu, David M. Ruppert
Johns Hopkins University, Dept. of Biostatistics Working Papers
The paper introduces a general framework for testing hypotheses about the structure of the mean function of complex functional processes. Important particular cases of the proposed framework are: 1) testing the null hypotheses that the mean of a functional process is parametric against a nonparametric alternative; and 2) testing the null hypothesis that the means of two possibly correlated functional processes are equal or differ by only a simple parametric function. A global pseudo likelihood ratio test is proposed and its asymptotic distribution is derived. The size and power properties of the test are confirmed in realistic simulation scenarios. Finite …
Longitudinal Functional Models With Structured Penalties, Madan G. Kundu, Jaroslaw Harezlak, Timothy W. Randolph
Longitudinal Functional Models With Structured Penalties, Madan G. Kundu, Jaroslaw Harezlak, Timothy W. Randolph
Johns Hopkins University, Dept. of Biostatistics Working Papers
Collection of functional data is becoming increasingly common including longitudinal observations in many studies. For example, we use magnetic resonance (MR) spectra collected over a period of time from late stage HIV patients. MR spectroscopy (MRS) produces a spectrum which is a mixture of metabolite spectra, instrument noise and baseline profile. Analysis of such data typically proceeds in two separate steps: feature extraction and regression modeling. In contrast, a recently-proposed approach, called partially empirical eigenvectors for regression (PEER) (Randolph, Harezlak and Feng, 2012), for functional linear models incorporates a priori knowledge via a scientifically-informed penalty operator in the regression function …
Modeling Sleep Fragmentation In Populations Of Sleep Hypnograms, Bruce J. Swihart, Naresh M. Punjabi, Ciprian M. Crainiceanu
Modeling Sleep Fragmentation In Populations Of Sleep Hypnograms, Bruce J. Swihart, Naresh M. Punjabi, Ciprian M. Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
We introduce methods for the analysis of large populations of sleep architectures (hypnograms) that respect the 5-state 20-transition-type structure defined by the American Academy of Sleep Medicine. By applying these methods to the hypnograms of 5598 subjects from the Sleep Heart Health Study we: 1) provide the firrst analysis of sleep hypnogram data of such size and complexity in a community cohort with a 4-level comorbidity; 2) compare 5-state 20-transition-type sleep to 3-state 6-transition-type sleep for a check of feasibility and information-loss; 3) extend current approaches to multivariate survival data analysis to populations of time-to-transition processes; and 4) provide scalable …
Analytic Programming With Fmri Data: A Quick-Start Guide For Statisticians Using R, Ani Eloyan, Shanshan Li, John Muschelli, Jim Pekar, Stewart Mostofsky, Brian S. Caffo
Analytic Programming With Fmri Data: A Quick-Start Guide For Statisticians Using R, Ani Eloyan, Shanshan Li, John Muschelli, Jim Pekar, Stewart Mostofsky, Brian S. Caffo
Johns Hopkins University, Dept. of Biostatistics Working Papers
Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project along with the relevant R code. The goal is to give tatisticians who would like to pursue research in this area a quick start for programming with fMRI data along with the available data visualization tools.
Confidence Intervals For The Selected Population In Randomized Trials That Adapt The Population Enrolled, Michael Rosenblum
Confidence Intervals For The Selected Population In Randomized Trials That Adapt The Population Enrolled, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
It is a challenge to design randomized trials when it is suspected that a treatment may benefit only certain subsets of the target population. In such situations, trial designs have been proposed that modify the population enrolled based on an interim analysis, in a preplanned manner. For example, if there is early evidence that the treatment only benefits a certain subset of the population, enrollment may then be restricted to this subset. At the end of such a trial, it is desirable to draw inferences about the selected population. We focus on constructing confidence intervals for the average treatment effect …
Automated Diagnoses Of Attention Deficit Hyperactive Disorder Using Magnetic Resonance Imaging, Ani Eloyan, John Muschelli, Mary Beth Nebel, Han Liu, Fang Han, Tuo Zhao, Anita Barber, Suresh Joel, James J. Pekar, Stewart Mostofsky, Brian Caffo
Automated Diagnoses Of Attention Deficit Hyperactive Disorder Using Magnetic Resonance Imaging, Ani Eloyan, John Muschelli, Mary Beth Nebel, Han Liu, Fang Han, Tuo Zhao, Anita Barber, Suresh Joel, James J. Pekar, Stewart Mostofsky, Brian Caffo
Johns Hopkins University, Dept. of Biostatistics Working Papers
Successful automated diagnoses of attention de.cit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scienti.c and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough …
Bootstrap-Based Inference On The Difference In The Means Of Two Correlated Functional Processes, Ciprian M. Crainiceanu, Ana-Maria Staicu, Shubankar Ray, Naresh Punjabi
Bootstrap-Based Inference On The Difference In The Means Of Two Correlated Functional Processes, Ciprian M. Crainiceanu, Ana-Maria Staicu, Shubankar Ray, Naresh Punjabi
Johns Hopkins University, Dept. of Biostatistics Working Papers
Nonparametric inference methods on the mean difference between two correlated Functional processes are proposed. We compare methods that: 1) incorporate different levels of smoothing of the mean and covariance; 2) preserve the sampling design; and 3) use parametric and nonparametric estimation of the mean functions. We apply our method to estimating the mean difference between average normalized δ-power of sleep electroencephalograms for 51 subjects with severe sleep apnea and 51 matched controls in the first 4 hours after sleep onset. Data are obtained from the Sleep Heart Health Study (SHHS), the largest community cohort study of sleep. While methods are …
Flexible Distributed Lag Models Using Random Functions With Application To Estimating Mortality Displacement From Heat-Related Deaths, Roger D. Peng
Flexible Distributed Lag Models Using Random Functions With Application To Estimating Mortality Displacement From Heat-Related Deaths, Roger D. Peng
Johns Hopkins University, Dept. of Biostatistics Working Papers
No abstract provided.
Longitudinal High-Dimensional Data Analysis, Vadim Zipunnikov, Sonja Greven, Brian Caffo, Daniel S. Reich, Ciprian Crainiceanu
Longitudinal High-Dimensional Data Analysis, Vadim Zipunnikov, Sonja Greven, Brian Caffo, Daniel S. Reich, Ciprian Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
We develop a flexible framework for modeling high-dimensional functional and imaging data observed longitudinally. The approach decomposes the observed variability of high-dimensional observations measured at multiple visits into three additive components: a subject-specific functional random intercept that quantifies the cross-sectional variability, a subject-specific functional slope that quantifies the dynamic irreversible deformation over multiple visits, and a subject-visit specific functional deviation that quantifies exchangeable or reversible visit-to-visit changes. The proposed method is very fast, scalable to studies including ultra-high dimensional data, and can easily be adapted to and executed on modest computing infrastructures. The method is applied to the longitudinal analysis …
Assessing Association For Bivariate Survival Data With Interval Sampling: A Copula Model Approach With Application To Aids Study, Hong Zhu, Mei-Cheng Wang
Assessing Association For Bivariate Survival Data With Interval Sampling: A Copula Model Approach With Application To Aids Study, Hong Zhu, Mei-Cheng Wang
Johns Hopkins University, Dept. of Biostatistics Working Papers
In disease surveillance systems or registries, bivariate survival data are typically collected under interval sampling. It refers to a situation when entry into a registry is at the time of the first failure event (e.g., HIV infection) within a calendar time interval, the time of the initiating event (e.g., birth) is retrospectively identified for all the cases in the registry, and subsequently the second failure event (e.g., death) is observed during the follow-up. Sampling bias is induced due to the selection process that the data are collected conditioning on the first failure event occurs within a time interval. Consequently, the …
Likelihood Based Population Independent Component Analysis, Ani Eloyan, Ciprian M. Crainiceanu, Brian S. Caffo
Likelihood Based Population Independent Component Analysis, Ani Eloyan, Ciprian M. Crainiceanu, Brian S. Caffo
Johns Hopkins University, Dept. of Biostatistics Working Papers
Independent component analysis (ICA) is a widely used technique for blind source separation, used heavily in several scientific research areas including acoustics, electrophysiology and functional neuroimaging. We propose a scalable two-stage iterative true group ICA methodology for analyzing population level fMRI data where the number of subjects is very large. The method is based on likelihood estimators of the underlying source densities and the mixing matrix. As opposed to many commonly used group ICA algorithms the proposed method does not require significant data reduction by a twofold singular value decomposition. In addition, the method can be applied to a large …
Corrected Confidence Bands For Functional Data Using Principal Components, Jeff Goldsmith, Sonja Greven, Ciprian M. Crainiceanu
Corrected Confidence Bands For Functional Data Using Principal Components, Jeff Goldsmith, Sonja Greven, Ciprian M. Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this paper, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- based and decomposition-based variability are constructed. Standard mixed-model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. A bootstrap procedure is implemented to understand the uncertainty in …
Longitudinal Analysis Of Spatiotemporal Processes: A Case Study Of Dynamic Contrast-Enhanced Magnetic Resonance Imaging In Multiple Sclerosis, Russell T. Shinohara, Ciprian M. Crainiceanu, Brian S. Caffo, Daniel S. Reich
Longitudinal Analysis Of Spatiotemporal Processes: A Case Study Of Dynamic Contrast-Enhanced Magnetic Resonance Imaging In Multiple Sclerosis, Russell T. Shinohara, Ciprian M. Crainiceanu, Brian S. Caffo, Daniel S. Reich
Johns Hopkins University, Dept. of Biostatistics Working Papers
Multiple sclerosis (MS) is an immune-mediated disease in which inflammatory lesions form in the brain. In many active MS lesions, the blood-brain barrier (BBB) is disrupted and blood flows into white matter; this disruption may be related to morbidity and disability. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows quantitative study of blood flow and permeability dynamics throughout the brain. This technique involves a subject being imaged sequentially during a study visit as an intravenously administered contrast agent flows into the brain. In regions where flow is abnormal, such as white matter lesions, this allows the quantification of the BBB damage. …
Movelets: A Dictionary Of Movement, Jiawei Bai, Jeff Goldsmith, Brian Caffo, Thomas A. Glass, Ciprian M. Crainiceanu
Movelets: A Dictionary Of Movement, Jiawei Bai, Jeff Goldsmith, Brian Caffo, Thomas A. Glass, Ciprian M. Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
Recent technological advances provide researchers a way of gathering real-time information on an individual’s movement through the use of wearable devices that record acceleration. In this paper, we propose a method for identifying activity types, like walking, standing, and resting, from acceleration data. Our approach decomposes movements into short components called “movelets”, and builds a reference for each activity type. Unknown activities are predicted by matching new movelets to the reference. We apply our method to data collected from a single, three-axis accelerometer and focus on activities of interest in studying physical function in elderly populations. An important technical advantage …
Reduced Bayesian Hierarchical Models: Estimating Health Effects Of Simultaneous Exposure To Multiple Pollutants, Jennifer F. Bobb, Francesca Dominici, Roger D. Peng
Reduced Bayesian Hierarchical Models: Estimating Health Effects Of Simultaneous Exposure To Multiple Pollutants, Jennifer F. Bobb, Francesca Dominici, Roger D. Peng
Johns Hopkins University, Dept. of Biostatistics Working Papers
Quantifying the health effects associated with simultaneous exposure to many air pollutants is now a research priority of the US EPA. Bayesian hierarchical models (BHM) have been extensively used in multisite time series studies of air pollution and health to estimate health effects of a single pollutant adjusted for potential confounding of other pollutants and other time-varying factors. However, when the scientific goal is to estimate the impacts of many pollutants jointly, a straightforward application of BHM is challenged by the need to specify a random-effect distribution on a high-dimensional vector of nuisance parameters, which often do not have an …
Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm, Hemant Pasusangai Kasturiwale
Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm, Hemant Pasusangai Kasturiwale
Johns Hopkins University, Dept. of Biostatistics Working Papers
Biomedical signals can arise from one or many sources including heart ,brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The Biomedical time series signal are like electroencephalogram(EEG),electrocardiogram(ECG),etc The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. The diagnosis of patient is based on visual observation of recorded ECG,EEG,etc, may not be accurate. To achieve better understanding , PCA (Principal Component Analysis) and ICA algorithms helps in analyzing ECG signals . The immense scope in the field of biomedical-signal processing Independent Component Analysis( …
A Broad Symmetry Criterion For Nonparametric Validity Of Parametrically-Based Tests In Randomized Trials, Russell T. Shinohara, Constantine E. Frangakis, Constantine G.. Lyketos
A Broad Symmetry Criterion For Nonparametric Validity Of Parametrically-Based Tests In Randomized Trials, Russell T. Shinohara, Constantine E. Frangakis, Constantine G.. Lyketos
Johns Hopkins University, Dept. of Biostatistics Working Papers
Summary. Pilot phases of a randomized clinical trial often suggest that a parametric model may be an accurate description of the trial's longitudinal trajectories. However, parametric models are often not used for fear that they may invalidate tests of null hypotheses of equality between the experimental groups. Existing work has shown that when, for some types of data, certain parametric models are used, the validity for testing the null is preserved even if the parametric models are incorrect. Here, we provide a broader and easier to check characterization of parametric models that can be used to (a) preserve nonparametric validity …