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Biometrical Journal. Biometrische Zeitschrift

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https://www.readbyqxmd.com/read/27886393/blinded-versus-unblinded-estimation-of-a-correlation-coefficient-to-inform-interim-design-adaptations
#1
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
#2
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
#3
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
#4
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
#5
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
#6
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/27902843/ridge-estimation-of-the-var-1-model-and-its-time-series-chain-graph-from-multivariate-time-course-omics-data
#7
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...
November 7, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27804147/treatment-of-nonignorable-missing-data-when-modeling-unobserved-heterogeneity-with-finite-mixture-models
#8
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...
November 2, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27775844/signal-localization-a-new-approach-in-signal-discovery
#9
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...
October 24, 2016: 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
#10
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...
October 24, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27763683/testing-interaction-between-treatment-and-high-dimensional-covariates-in-randomized-clinical-trials
#11
Andrea Callegaro, Bart Spiessens, Benjamin Dizier, Fernando U Montoya, Hans C van Houwelingen
In this paper, we considered different methods to test the interaction between treatment and a potentially large number (p) of covariates in randomized clinical trials. The simplest approach was to fit univariate (marginal) models and to combine the univariate statistics or p-values (e.g., minimum p-value). Another possibility was to reduce the dimension of the covariates using the principal components (PCs) and to test the interaction between treatment and PCs. Finally, we considered the Goeman global test applied to the high-dimensional interaction matrix, adjusted for the main (treatment and covariates) effects...
October 20, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27757980/interim-evaluation-of-efficacy-or-futility-in-group-sequential-trials-with-multiple-co-primary-endpoints
#12
Koko Asakura, Toshimitsu Hamasaki, Scott R Evans
We discuss group-sequential designs in superiority clinical trials with multiple co-primary endpoints, that is, when trials are designed to evaluate if the test intervention is superior to the control on all primary endpoints. We consider several decision-making frameworks for evaluating efficacy or futility, based on boundaries using group-sequential methodology. We incorporate the correlations among the endpoints into the calculations for futility boundaries and sample sizes as a function of other design parameters, including mean differences, the number of analyses, and efficacy boundaries...
October 19, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27754556/meta-analysis-of-two-studies-in-the-presence-of-heterogeneity-with-applications-in-rare-diseases
#13
Tim Friede, Christian Röver, Simon Wandel, Beat Neuenschwander
Random-effects meta-analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random-effects meta-analysis assumes approximately normal effect estimates and a normal random-effects model. However, standard methods based on this model ignore the uncertainty in estimating the between-trial heterogeneity...
October 18, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27740692/prioritizing-covariates-in-the-planning-of-future-studies-in-the-meta-analytic-framework
#14
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...
October 14, 2016: 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
#15
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)...
October 5, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27704593/design-of-clinical-trials-involving-multiple-hypothesis-tests-with-a-common-control
#16
I Manjula Schou, Ian C Marschner
Randomized clinical trials comparing several treatments to a common control are often reported in the medical literature. For example, multiple experimental treatments may be compared with placebo, or in combination therapy trials, a combination therapy may be compared with each of its constituent monotherapies. Such trials are typically designed using a balanced approach in which equal numbers of individuals are randomized to each arm, however, this can result in an inefficient use of resources. We provide a unified framework and new theoretical results for optimal design of such single-control multiple-comparator studies...
October 5, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27667731/bayesian-variable-selection-and-estimation-in-semiparametric-joint-models-of-multivariate-longitudinal-and-survival-data
#17
An-Min Tang, Xingqiu Zhao, Nian-Sheng Tang
This paper presents a novel semiparametric joint model for multivariate longitudinal and survival data (SJMLS) by relaxing the normality assumption of the longitudinal outcomes, leaving the baseline hazard functions unspecified and allowing the history of the longitudinal response having an effect on the risk of dropout. Using Bayesian penalized splines to approximate the unspecified baseline hazard function and combining the Gibbs sampler and the Metropolis-Hastings algorithm, we propose a Bayesian Lasso (BLasso) method to simultaneously estimate unknown parameters and select important covariates in SJMLS...
September 26, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27658370/one-inflation-and-unobserved-heterogeneity-in-population-size-estimation
#18
Ryan T Godwin
We present the one-inflated zero-truncated negative binomial (OIZTNB) model, and propose its use as the truncated count distribution in Horvitz-Thompson estimation of an unknown population size. In the presence of unobserved heterogeneity, the zero-truncated negative binomial (ZTNB) model is a natural choice over the positive Poisson (PP) model; however, when one-inflation is present the ZTNB model either suffers from a boundary problem, or provides extremely biased population size estimates. Monte Carlo evidence suggests that in the presence of one-inflation, the Horvitz-Thompson estimator under the ZTNB model can converge in probability to infinity...
September 23, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27645002/risk-adjusted-monitoring-of-time-to-event-in-the-presence-of-long-term-survivors
#19
Jocelânio W Oliveira, Dione M Valença, Pledson G Medeiros, Magaly Marçula
The use of control charts for monitoring schemes in medical context should consider adjustments to incorporate the specific risk for each individual. Some authors propose the use of a risk-adjusted survival time cumulative sum (RAST CUSUM) control chart to monitor a time-to-event outcome, possibly right censored, using conventional survival models, which do not contemplate the possibility of cure of a patient. We propose to extend this approach considering a risk-adjusted CUSUM chart, based on a cure rate model...
September 20, 2016: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/27627622/short-term-and-long-term-effects-of-acute-kidney-injury-in-chronic-kidney-disease-patients-a-longitudinal-analysis
#20
Özgür Asar, James Ritchie, Philip A Kalra, Peter J Diggle
We use data from an ongoing cohort study of chronic kidney patients at Salford Royal NHS Foundation Trust, Greater Manchester, United Kingdom, to investigate the influence of acute kidney injury (AKI) on the subsequent rate of change of kidney function amongst patients already diagnosed with chronic kidney disease (CKD). We use a linear mixed effects modelling framework to enable estimation of both acute and chronic effects of AKI events on kidney function. We model the fixed effects by a piece-wise linear function with three change-points to capture the acute changes in kidney function that characterise an AKI event, and the random effects by the sum of three components: a random intercept, a stationary stochastic process with Matérn correlation structure, and measurement error...
September 14, 2016: Biometrical Journal. Biometrische Zeitschrift
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