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Biometrics

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https://www.readbyqxmd.com/read/28504836/simple-and-fast-overidentified-rank-estimation-for-right-censored-length-biased-data-and-backward-recurrence-time
#1
Yifei Sun, Kwun Chuen Gary Chan, Jing Qin
Length-biased survival data subject to right-censoring are often collected from a prevalent cohort. However, informative right censoring induced by the sampling design creates challenges in methodological development. While certain conditioning arguments could circumvent the problem of informative censoring, related rank estimation methods are typically inefficient because the marginal likelihood of the backward recurrence time is not ancillary. Under a semiparametric accelerated failure time model, an overidentified set of log-rank estimating equations is constructed based on the left-truncated right-censored data and backward recurrence time...
May 15, 2017: Biometrics
https://www.readbyqxmd.com/read/28504821/why-you-cannot-transform-your-way-out-of-trouble-for-small-counts
#2
David I Warton
While data transformation is a common strategy to satisfy linear modeling assumptions, a theoretical result is used to show that transformation cannot reasonably be expected to stabilize variances for small counts. Under broad assumptions, as counts get smaller, it is shown that the variance becomes proportional to the mean under monotonic transformations g(·) that satisfy g(0)=0, excepting a few pathological cases. A suggested rule-of-thumb is that if many predicted counts are less than one then data transformation cannot reasonably be expected to stabilize variances, even for a well-chosen transformation...
May 15, 2017: Biometrics
https://www.readbyqxmd.com/read/28498564/spatial-bayesian-latent-factor-regression-modeling-of-coordinate-based-meta-analysis-data
#3
Silvia Montagna, Tor Wager, Lisa Feldman Barrett, Timothy D Johnson, Thomas E Nichols
Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA...
May 12, 2017: Biometrics
https://www.readbyqxmd.com/read/28498490/multi-subgroup-gene-screening-using-semi-parametric-hierarchical-mixture-models-and-the-optimal-discovery-procedure-application-to-a-randomized-clinical-trial-in-multiple-myeloma
#4
Shigeyuki Matsui, Hisashi Noma, Pingping Qu, Yoshio Sakai, Kota Matsui, Christoph Heuck, John Crowley
This article proposes an efficient approach to screening genes associated with a phenotypic variable of interest in genomic studies with subgroups. In order to capture and detect various association profiles across subgroups, we flexibly estimate the underlying effect size distribution across subgroups using a semi-parametric hierarchical mixture model for subgroup-specific summary statistics from independent subgroups. We then perform gene ranking and selection using an optimal discovery procedure based on the fitted model with control of false discovery rate...
May 12, 2017: Biometrics
https://www.readbyqxmd.com/read/28493315/robust-mislabel-logistic-regression-without-modeling-mislabel-probabilities
#5
Hung Hung, Zhi-Yu Jou, Su-Yun Huang
Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence...
May 10, 2017: Biometrics
https://www.readbyqxmd.com/read/28493302/instrumental-variables-estimation-of-exposure-effects-on-a-time-to-event-endpoint-using-structural-cumulative-survival-models
#6
Torben Martinussen, Stijn Vansteelandt, Eric J Tchetgen Tchetgen, David M Zucker
The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes...
May 10, 2017: Biometrics
https://www.readbyqxmd.com/read/28482133/estimating-the-probability-of-clonal-relatedness-of-pairs-of-tumors-in-cancer-patients
#7
Audrey Mauguen, Venkatraman E Seshan, Irina Ostrovnaya, Colin B Begg
Next generation sequencing panels are being used increasingly in cancer research to study tumor evolution. A specific statistical challenge is to compare the mutational profiles in different tumors from a patient to determine the strength of evidence that the tumors are clonally related, that is, derived from a single, founder clonal cell. The presence of identical mutations in each tumor provides evidence of clonal relatedness, although the strength of evidence from a match is related to how commonly the mutation is seen in the tumor type under investigation...
May 8, 2017: Biometrics
https://www.readbyqxmd.com/read/28482128/hidden-markov-models-for-extended-batch-data
#8
Laura L E Cowen, Panagiotis Besbeas, Byron J T Morgan, Carl J Schwarz
Batch marking provides an important and efficient way to estimate the survival probabilities and population sizes of wild animals. It is particularly useful when dealing with animals that are difficult to mark individually. For the first time, we provide the likelihood for extended batch-marking experiments. It is often the case that samples contain individuals that remain unmarked, due to time and other constraints, and this information has not previously been analyzed. We provide ways of modeling such information, including an open N-mixture approach...
May 8, 2017: Biometrics
https://www.readbyqxmd.com/read/28482123/integrative-analysis-of-transcriptomic-and-metabolomic-data-via-sparse-canonical-correlation-analysis-with-incorporation-of-biological-information
#9
Sandra E Safo, Shuzhao Li, Qi Long
Integrative analysis of high dimensional omics data is becoming increasingly popular. At the same time, incorporating known functional relationships among variables in analysis of omics data has been shown to help elucidate underlying mechanisms for complex diseases. In this article, our goal is to assess association between transcriptomic and metabolomic data from a Predictive Health Institute (PHI) study that includes healthy adults at a high risk of developing cardiovascular diseases. Adopting a strategy that is both data-driven and knowledge-based, we develop statistical methods for sparse canonical correlation analysis (CCA) with incorporation of known biological information...
May 8, 2017: Biometrics
https://www.readbyqxmd.com/read/28482120/fpca-based-method-to-select-optimal-sampling-schedules-that-capture-between-subject-variability-in-longitudinal-studies
#10
Meihua Wu, Ana Diez-Roux, Trivellore E Raghunathan, Brisa N Sánchez
A critical component of longitudinal study design involves determining the sampling schedule. Criteria for optimal design often focus on accurate estimation of the mean profile, although capturing the between-subject variance of the longitudinal process is also important since variance patterns may be associated with covariates of interest or predict future outcomes. Existing design approaches have limited applicability when one wishes to optimize sampling schedules to capture between-individual variability...
May 8, 2017: Biometrics
https://www.readbyqxmd.com/read/28482112/a-bayesian-screening-approach-for-hepatocellular-carcinoma-using-multiple-longitudinal-biomarkers
#11
Nabihah Tayob, Francesco Stingo, Kim-Anh Do, Anna S F Lok, Ziding Feng
Advanced hepatocellular carcinoma (HCC) has limited treatment options and poor survival, therefore early detection is critical to improving the survival of patients with HCC. Current guidelines for high-risk patients include ultrasound screenings every six months, but ultrasounds are operator dependent and not sensitive for early HCC. Serum α-Fetoprotein (AFP) is a widely used diagnostic biomarker, but it has limited sensitivity and is not elevated in all HCC cases so, we incorporate a second blood-based biomarker, des'γ carboxy-prothrombin (DCP), that has shown potential as a screening marker for HCC...
May 8, 2017: Biometrics
https://www.readbyqxmd.com/read/28482111/conditional-adaptive-bayesian-spectral-analysis-of-nonstationary-biomedical-time-series
#12
Scott A Bruce, Martica H Hall, Daniel J Buysse, Robert T Krafty
Many studies of biomedical time series signals aim to measure the association between frequency-domain properties of time series and clinical and behavioral covariates. However, the time-varying dynamics of these associations are largely ignored due to a lack of methods that can assess the changing nature of the relationship through time. This article introduces a method for the simultaneous and automatic analysis of the association between the time-varying power spectrum and covariates, which we refer to as conditional adaptive Bayesian spectrum analysis (CABS)...
May 8, 2017: Biometrics
https://www.readbyqxmd.com/read/28482110/functional-multiple-indicators-multiple-causes-measurement-error-models
#13
Carmen D Tekwe, Roger S Zoh, Fuller W Bazer, Guoyao Wu, Raymond J Carroll
Objective measures of oxygen consumption and carbon dioxide production by mammals are used to predict their energy expenditure. Since energy expenditure is not directly observable, it can be viewed as a latent construct with multiple physical indirect measures such as respiratory quotient, volumetric oxygen consumption, and volumetric carbon dioxide production. Metabolic rate is defined as the rate at which metabolism occurs in the body. Metabolic rate is also not directly observable. However, heat is produced as a result of metabolic processes within the body...
May 8, 2017: Biometrics
https://www.readbyqxmd.com/read/28452052/computation-of-ancestry-scores-with-mixed-families-and-unrelated-individuals
#14
Yi-Hui Zhou, James S Marron, Fred A Wright
The issue of robustness to family relationships in computing genotype ancestry scores such as eigenvector projections has received increased attention in genetic association, and is particularly challenging when sets of both unrelated individuals and closely related family members are included. The current standard is to compute loadings (left singular vectors) using unrelated individuals and to compute projected scores for remaining family members. However, projected ancestry scores from this approach suffer from shrinkage toward zero...
April 27, 2017: Biometrics
https://www.readbyqxmd.com/read/28444692/a-profile-likelihood-approach-for-longitudinal-data-analysis
#15
Ziqi Chen, Man-Lai Tang, Wei Gao
Inappropriate choice of working correlation structure in generalized estimating equations (GEE) could lead to inefficient parameter estimation while impractical normality assumption in likelihood approach would limit its applicability in longitudinal data analysis. In this article, we propose a profile likelihood method for estimating parameters in longitudinal data analysis via maximizing the estimated likelihood. The proposed method yields consistent and efficient estimates without specifications of the working correlation structure nor the underlying error distribution...
April 25, 2017: Biometrics
https://www.readbyqxmd.com/read/28444688/semiparametric-estimation-of-the-accelerated-failure-time-model-with-partly-interval-censored-data
#16
Fei Gao, Donglin Zeng, Dan-Yu Lin
Partly interval-censored (PIC) data arise when some failure times are exactly observed while others are only known to lie within certain intervals. In this article, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with PIC data. We first generalize the Buckley-James estimator for right-censored data to PIC data. Then, we develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We show that under mild regularity conditions the generalized Buckley-James estimator is consistent and asymptotically normal and the one-step estimator is consistent and asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound...
April 25, 2017: Biometrics
https://www.readbyqxmd.com/read/28437848/inferring-network-structure-in-non-normal-and-mixed-discrete-continuous-genomic-data
#17
Anindya Bhadra, Arvind Rao, Veerabhadran Baladandayuthapani
Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate...
April 24, 2017: Biometrics
https://www.readbyqxmd.com/read/28437561/semiparametric-probit-models-with-univariate-and-bivariate-current-status-data
#18
Hao Liu, Jing Qin
Multivariate current-status data are frequently encountered in biomedical and public health studies. Semiparametric regression models have been extensively studied for univariate current-status data, but most existing estimation procedures are computationally intensive, involving either penalization or smoothing techniques. It becomes more challenging for the analysis of multivariate current-status data. In this article, we study the maximum likelihood estimations for univariate and bivariate current-status data under the semiparametric probit regression models...
April 24, 2017: Biometrics
https://www.readbyqxmd.com/read/28426896/a-note-on-marginalization-of-regression-parameters-from-mixed-models-of-binary-outcomes
#19
Donald Hedeker, Stephen H C du Toit, Hakan Demirtas, Robert D Gibbons
This article discusses marginalization of the regression parameters in mixed models for correlated binary outcomes. As is well known, the regression parameters in such models have the "subject-specific" (SS) or conditional interpretation, in contrast to the "population-averaged" (PA) or marginal estimates that represent the unconditional covariate effects. We describe an approach using numerical quadrature to obtain PA estimates from their SS counterparts in models with multiple random effects. Standard errors for the PA estimates are derived using the delta method...
April 20, 2017: Biometrics
https://www.readbyqxmd.com/read/28407218/incorporating-covariates-into-integrated-factor-analysis-of-multi-view-data
#20
Gen Li, Sungkyu Jung
In modern biomedical research, it is ubiquitous to have multiple data sets measured on the same set of samples from different views (i.e., multi-view data). For example, in genetic studies, multiple genomic data sets at different molecular levels or from different cell types are measured for a common set of individuals to investigate genetic regulation. Integration and reduction of multi-view data have the potential to leverage information in different data sets, and to reduce the magnitude and complexity of data for further statistical analysis and interpretation...
April 13, 2017: Biometrics
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