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Biometrical Journal. Biometrische Zeitschrift

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https://www.readbyqxmd.com/read/28054373/birnbaum-saunders-frailty-regression-models-diagnostics-and-application-to-medical-data
#1
Jeremias Leão, Víctor Leiva, Helton Saulo, Vera Tomazella
In survival models, some covariates affecting the lifetime could not be observed or measured. These covariates may correspond to environmental or genetic factors and be considered as a random effect related to a frailty of the individuals explaining their survival times. We propose a methodology based on a Birnbaum-Saunders frailty regression model, which can be applied to censored or uncensored data. Maximum-likelihood methods are used to estimate the model parameters and to derive local influence techniques...
January 5, 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/28025852/multiple-confidence-intervals-for-selected-parameters-adjusted-for-the-false-coverage-rate-in-monotone-dose-response-microarray-experiments
#2
Jianan Peng, Wei Liu, Frank Bretz, Ziv Shkedy
Benjamini and Yekutieli () introduced the concept of the false coverage-statement rate (FCR) to account for selection when the confidence intervals (CIs) are constructed only for the selected parameters. Dose-response analysis in dose-response microarray experiments is conducted only for genes having monotone dose-response relationship, which is a selection problem. In this paper, we consider multiple CIs for the mean gene expression difference between the highest dose and control in monotone dose-response microarray experiments for selected parameters adjusted for the FCR...
December 26, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/28025850/estimating-latent-trends-in-multivariate-longitudinal-data-via-parafac2-with-functional-and-structural-constraints
#3
Nathaniel E Helwig
Longitudinal data are inherently multimode in the sense that such data are often collected across multiple modes of variation, for example, time × variables × subjects. In many longitudinal studies, multiple variables are collected to measure some latent construct(s) of interest. In such cases, the goal is to understand temporal trends in the latent variables, as well as individual differences in the trends. Multimode component analysis models provide a powerful framework for discovering latent trends in longitudinal data...
December 26, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/28008645/confidence-intervals-for-directly-standardized-rates-using-mid-p-gamma-intervals
#4
Michael P Fay, Sungwook Kim
Directly standardized rates continue to be an integral tool for presenting rates for diseases that are highly dependent on age, such as cancer. Statistically, these rates are modeled as a weighted sum of Poisson random variables. This is a difficult statistical problem, because there are k observed Poisson variables and k unknown means. The gamma confidence interval has been shown through simulations to have at least nominal coverage in all simulated scenarios, but it can be overly conservative. Previous modifications to that method have closer to nominal coverage on average, but they do not achieve the nominal coverage bound in all situations...
December 23, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27983754/estimating-the-prevalence-of-atrial-fibrillation-from-a-three-class-mixture-model-for-repeated-diagnoses
#5
Liang Li, Huzhang Mao, Hemant Ishwaran, Jeevanantham Rajeswaran, John Ehrlinger, Eugene H Blackstone
Atrial fibrillation (AF) is an abnormal heart rhythm characterized by rapid and irregular heartbeat, with or without perceivable symptoms. In clinical practice, the electrocardiogram (ECG) is often used for diagnosis of AF. Since the AF often arrives as recurrent episodes of varying frequency and duration and only the episodes that occur at the time of ECG can be detected, the AF is often underdiagnosed when a limited number of repeated ECGs are used. In studies evaluating the efficacy of AF ablation surgery, each patient undergoes multiple ECGs and the AF status at the time of ECG is recorded...
December 16, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27886393/blinded-versus-unblinded-estimation-of-a-correlation-coefficient-to-inform-interim-design-adaptations
#6
Cornelia U Kunz, Nigel Stallard, Nicholas Parsons, Susan Todd, Tim Friede
Regulatory authorities require that the sample size of a confirmatory trial is calculated prior to the start of the trial. However, the sample size quite often depends on parameters that might not be known in advance of the study. Misspecification of these parameters can lead to under- or overestimation of the sample size. Both situations are unfavourable as the first one decreases the power and the latter one leads to a waste of resources. Hence, designs have been suggested that allow a re-assessment of the sample size in an ongoing trial...
November 25, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27878856/a-semiparametric-mixture-cure-survival-model-for-left-truncated-and-right-censored-data
#7
Chyong-Mei Chen, Pao-Sheng Shen, James Cheng-Chung Wei, Lichi Lin
In follow-up studies, the disease event time can be subject to left truncation and right censoring. Furthermore, medical advancements have made it possible for patients to be cured of certain types of diseases. In this article, we consider a semiparametric mixture cure model for the regression analysis of left-truncated and right-censored data. The model combines a logistic regression for the probability of event occurrence with the class of transformation models for the time of occurrence. We investigate two techniques for estimating model parameters...
November 23, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27878852/optimal-design-of-longitudinal-data-analysis-using-generalized-estimating-equation-models
#8
Jingxia Liu, Graham A Colditz
Longitudinal studies are often applied in biomedical research and clinical trials to evaluate the treatment effect. The association pattern within the subject must be considered in both sample size calculation and the analysis. One of the most important approaches to analyze such a study is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which "working correlation structure" is introduced and the association pattern within the subject depends on a vector of association parameters denoted by ρ...
November 23, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27870130/estimating-hazard-ratios-in-cohort-data-with-missing-disease-information-due-to-death
#9
Nadine Binder, Anne-Sophie Herrnböck, Martin Schumacher
In clinical and epidemiological studies information on the primary outcome of interest, that is, the disease status, is usually collected at a limited number of follow-up visits. The disease status can often only be retrieved retrospectively in individuals who are alive at follow-up, but will be missing for those who died before. Right-censoring the death cases at the last visit (ad-hoc analysis) yields biased hazard ratio estimates of a potential risk factor, and the bias can be substantial and occur in either direction...
November 21, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27870109/analyzing-large-datasets-with-bootstrap-penalization
#10
Kuangnan Fang, Shuangge Ma
Data with a large p (number of covariates) and/or a large n (sample size) are now commonly encountered. For many problems, regularization especially penalization is adopted for estimation and variable selection. The straightforward application of penalization to large datasets demands a "big computer" with high computational power. To improve computational feasibility, we develop bootstrap penalization, which dissects a big penalized estimation into a set of small ones, which can be executed in a highly parallel manner and each only demands a "small computer"...
November 21, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27862181/identification-of-biomarker-by-treatment-interactions-in-randomized-clinical-trials-with-survival-outcomes-and-high-dimensional-spaces
#11
Nils Ternès, Federico Rotolo, Georg Heinze, Stefan Michiels
Stratified medicine seeks to identify biomarkers or parsimonious gene signatures distinguishing patients that will benefit most from a targeted treatment. We evaluated 12 approaches in high-dimensional Cox models in randomized clinical trials: penalization of the biomarker main effects and biomarker-by-treatment interactions (full-lasso, three kinds of adaptive lasso, ridge+lasso and group-lasso); dimensionality reduction of the main effect matrix via linear combinations (PCA+lasso (where PCA is principal components analysis) or PLS+lasso (where PLS is partial least squares)); penalization of modified covariates or of the arm-specific biomarker effects (two-I model); gradient boosting; and univariate approach with control of multiple testing...
November 15, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/28036125/contents-biometrical-journal-7-17
#12
(no author information available yet)
No abstract text is available yet for this article.
January 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/28036124/editorial-board-biometrical-journal-7-17
#13
(no author information available yet)
No abstract text is available yet for this article.
January 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/28036123/masthead-biometrical-journal-7-17
#14
(no author information available yet)
No abstract text is available yet for this article.
January 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27902843/ridge-estimation-of-the-var-1-model-and-its-time-series-chain-graph-from-multivariate-time-course-omics-data
#15
Viktorian Miok, Saskia M Wilting, Wessel N van Wieringen
Omics experiments endowed with a time-course design may enable us to uncover the dynamic interplay among genes of cellular processes. Multivariate techniques (like VAR(1) models describing the temporal and contemporaneous relations among variates) that may facilitate this goal are hampered by the high-dimensionality of the resulting data. This is resolved by the presented ridge regularized maximum likelihood estimation procedure for the VAR(1) model. Information on the absence of temporal and contemporaneous relations may be incorporated in this procedure...
January 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27804147/treatment-of-nonignorable-missing-data-when-modeling-unobserved-heterogeneity-with-finite-mixture-models
#16
Thomas Lehmann, Peter Schlattmann
Multiple imputation has become a widely accepted technique to deal with the problem of incomplete data. Typically, imputation of missing values and the statistical analysis are performed separately. Therefore, the imputation model has to be consistent with the analysis model. If the data are analyzed with a mixture model, the parameter estimates are usually obtained iteratively. Thus, if the data are missing not at random, parameter estimation and treatment of missingness should be combined. We solve both problems by simultaneously imputing values using the data augmentation method and estimating parameters using the EM algorithm...
January 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27775844/signal-localization-a-new-approach-in-signal-discovery
#17
Sergey V Malov, Alexey Antonik, Minzhong Tang, Alexandre Berred, Yi Zeng, Stephen J O'Brien
A new approach for statistical association signal identification is developed in this paper. We consider a strategy for nonprecise signal identification by extending the well-known signal detection and signal identification methods applicable to the multiple testing problem. Collection of statistical instruments under the presented approach is much broader than under the traditional signal identification methods, allowing more efficient signal discovery. Further assessments of maximal value and average statistics in signal discovery are improved...
January 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27774639/a-comparison-of-likelihood-ratio-tests-and-rao-s-score-test-for-three-separable-covariance-matrix-structures
#18
Katarzyna Filipiak, Daniel Klein, Anuradha Roy
The problem of testing the separability of a covariance matrix against an unstructured variance-covariance matrix is studied in the context of multivariate repeated measures data using Rao's score test (RST). The RST statistic is developed with the first component of the separable structure as a first-order autoregressive (AR(1)) correlation matrix or an unstructured (UN) covariance matrix under the assumption of multivariate normality. It is shown that the distribution of the RST statistic under the null hypothesis of any separability does not depend on the true values of the mean or the unstructured components of the separable structure...
January 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27740692/prioritizing-covariates-in-the-planning-of-future-studies-in-the-meta-analytic-framework
#19
Juha Karvanen, Mikko J Sillanpää
Science can be seen as a sequential process where each new study augments evidence to the existing knowledge. To have the best prospects to make an impact in this process, a new study should be designed optimally taking into account the previous studies and other prior information. We propose a formal approach for the covariate prioritization, that is the decision about the covariates to be measured in a new study. The decision criteria can be based on conditional power, change of the p-value, change in lower confidence limit, Kullback-Leibler divergence, Bayes factors, Bayesian false discovery rate or difference between prior and posterior expectation...
January 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27704599/a-zero-augmented-generalized-gamma-regression-calibration-to-adjust-for-covariate-measurement-error-a-case-of-an-episodically-consumed-dietary-intake
#20
George O Agogo
Measurement error in exposure variables is a serious impediment in epidemiological studies that relate exposures to health outcomes. In nutritional studies, interest could be in the association between long-term dietary intake and disease occurrence. Long-term intake is usually assessed with food frequency questionnaire (FFQ), which is prone to recall bias. Measurement error in FFQ-reported intakes leads to bias in parameter estimate that quantifies the association. To adjust for bias in the association, a calibration study is required to obtain unbiased intake measurements using a short-term instrument such as 24-hour recall (24HR)...
January 2017: Biometrical Journal. Biometrische Zeitschrift
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