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    1049 results match your criteria Biometrical Journal [Journal]

    1 OF 21

    Two-stage model for multivariate longitudinal and survival data with application to nephrology research.
    Biom J 2017 Nov;59(6):1204-1220
    Instituto de Ciencias Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal.
    In many follow-up studies different types of outcomes are collected including longitudinal measurements and time-to-event outcomes. Commonly, it is of interest to study the association between them. Joint modeling approaches of a single longitudinal outcome and survival process have recently gained increasing attention from both frequentist and Bayesian perspective. Read More

    H-likelihood approach for joint modeling of longitudinal outcomes and time-to-event data.
    Biom J 2017 Nov;59(6):1122-1143
    Department of Statistics, Seoul National University, Seoul, 151-742, South Korea.
    In longitudinal studies, a subject may have different types of outcomes that could be correlated. For example, a response variable of interest would be measured repeatedly over time on the same subject and at the same time, an event time representing a single event or competing-risks event is also observed. In this paper, we propose a joint modeling framework that accounts for the inherent association between such multiple outcomes via frailties (unobserved random effects). Read More

    A Bayesian scoring rule on clustered event data for familial risk assessment - An example from colorectal cancer screening.
    Biom J 2017 Nov 8. Epub 2017 Nov 8.
    Institute for Medical Information Sciences, Biometry and Epidemiology (IBE), Ludwig-Maximilians-Universität München, München, Germany.
    Colorectal cancer screening is well established. The identification of high risk populations is the key to implement effective risk-adjusted screening. Good statistical approaches for risk prediction do not exist. Read More

    A general framework for constraint approaches to adjusted risk differences.
    Biom J 2017 Nov 7. Epub 2017 Nov 7.
    Saint Luke's Mid America Heart Institute, Saint Luke's Health System, Kansas City, MO, USA.
    The risk difference is an intelligible measure for comparing disease incidence in two exposure or treatment groups. Despite its convenience in interpretation, it is less prevalent in epidemiological and clinical areas where regression models are required in order to adjust for confounding. One major barrier to its popularity is that standard linear binomial or Poisson regression models can provide estimated probabilities out of the range of (0,1), resulting in possible convergence issues. Read More

    Asymptotic distributions of kappa statistics and their differences with many raters, many rating categories and two conditions.
    Biom J 2017 Nov 7. Epub 2017 Nov 7.
    Politecnico di Torino, Department of Mathematical Sciences, Torino, Italy.
    In clinical research and in more general classification problems, a frequent concern is the reliability of a rating system. In the absence of a gold standard, agreement may be considered as an indication of reliability. When dealing with categorical data, the well-known kappa statistic is often used to measure agreement. Read More

    Test-compatible confidence intervals for adaptive two-stage single-arm designs with binary endpoint.
    Biom J 2017 Oct 27. Epub 2017 Oct 27.
    Institute of Medical Biometry and Informatics, University of Heidelberg, Marsilius Arkaden, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany.
    Inference after two-stage single-arm designs with binary endpoint is challenging due to the nonunique ordering of the sampling space in multistage designs. We illustrate the problem of specifying test-compatible confidence intervals for designs with nonconstant second-stage sample size and present two approaches that guarantee confidence intervals consistent with the test decision. Firstly, we extend the well-known Clopper-Pearson approach of inverting a family of two-sided hypothesis tests from the group-sequential case to designs with fully adaptive sample size. Read More

    Estimating multiple time-fixed treatment effects using a semi-Bayes semiparametric marginal structural Cox proportional hazards regression model.
    Biom J 2017 Oct 27. Epub 2017 Oct 27.
    Departments of Epidemiology and Statistics, UCLA, Los Angeles, CA, USA.
    Marginal structural models for time-fixed treatments fit using inverse-probability weighted estimating equations are increasingly popular. Nonetheless, the resulting effect estimates are subject to finite-sample bias when data are sparse, as is typical for large-sample procedures. Here we propose a semi-Bayes estimation approach which penalizes or shrinks the estimated model parameters to improve finite-sample performance. Read More

    Two-stage orthogonality based estimation for semiparametric varying-coefficient models and its applications in analyzing AIDS data.
    Biom J 2017 Oct 26. Epub 2017 Oct 26.
    Department of Statistics, Nanjing Audit University, Nanjing, 211815, P. R., China.
    Semiparametric smoothing methods are usually used to model longitudinal data, and the interest is to improve efficiency for regression coefficients. This paper is concerned with the estimation in semiparametric varying-coefficient models (SVCMs) for longitudinal data. By the orthogonal projection method, local linear technique, quasi-score estimation, and quasi-maximum likelihood estimation, we propose a two-stage orthogonality-based method to estimate parameter vector, coefficient function vector, and covariance function. Read More

    Simulation-based evaluation of the linear-mixed model in the presence of an increasing proportion of singletons.
    Biom J 2017 Oct 25. Epub 2017 Oct 25.
    Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BIOSTAT), Hasselt University, Diepenbeek, Belgium.
    Data in medical sciences often have a hierarchical structure with lower level units (e.g. children) nested in higher level units (e. Read More

    Prediction errors for state occupation and transition probabilities in multi-state models.
    Biom J 2017 Oct 25. Epub 2017 Oct 25.
    Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
    In this paper, we consider the estimation of prediction errors for state occupation probabilities and transition probabilities for multistate time-to-event data. We study prediction errors based on the Brier score and on the Kullback-Leibler score and prove their properness. In the presence of right-censored data, two classes of estimators, based on inverse probability weighting and pseudo-values, respectively, are proposed, and consistency properties of the proposed estimators are investigated. Read More

    A comparison of different ways of including baseline counts in negative binomial models for data from falls prevention trials.
    Biom J 2017 Oct 25. Epub 2017 Oct 25.
    Medical Statistics Group, Faculty of Medicine, University of Southampton, Southampton, England.
    A common design for a falls prevention trial is to assess falling at baseline, randomize participants into an intervention or control group, and ask them to record the number of falls they experience during a follow-up period of time. This paper addresses how best to include the baseline count in the analysis of the follow-up count of falls in negative binomial (NB) regression. We examine the performance of various approaches in simulated datasets where both counts are generated from a mixed Poisson distribution with shared random subject effect. Read More

    A probabilistic network for the diagnosis of acute cardiopulmonary diseases.
    Biom J 2017 Oct 13. Epub 2017 Oct 13.
    Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy.
    In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented in detail. A panel of expert physicians collaborated to specify the qualitative part, which is a directed acyclic graph defining a factorization of the joint probability distribution of domain variables into univariate conditional distributions. The quantitative part, which is a set of parametric models defining these univariate conditional distributions, was estimated following the Bayesian paradigm. Read More

    Selection of composite binary endpoints in clinical trials.
    Biom J 2017 Oct 12. Epub 2017 Oct 12.
    Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, 08034, Barcelona, Spain.
    The choice of a primary endpoint is an important issue when designing a clinical trial. It is common to use composite endpoints as a primary endpoint because it increases the number of observed events, captures more information and is expected to increase the power. However, combining events that have no similar clinical importance and have different treatment effects makes the interpretation of the results cumbersome and might reduce the power of the corresponding tests. Read More

    Hierarchical imputation of systematically and sporadically missing data: An approximate Bayesian approach using chained equations.
    Biom J 2017 Oct 9. Epub 2017 Oct 9.
    Department of Methodology and Statistics, CAPHRI, Maastricht University, 6229, HA, Maastricht, The Netherlands.
    In health and medical sciences, multiple imputation (MI) is now becoming popular to obtain valid inferences in the presence of missing data. However, MI of clustered data such as multicenter studies and individual participant data meta-analysis requires advanced imputation routines that preserve the hierarchical structure of data. In clustered data, a specific challenge is the presence of systematically missing data, when a variable is completely missing in some clusters, and sporadically missing data, when it is partly missing in some clusters. Read More

    Multiple sensitive estimation and optimal sample size allocation in the item sum technique.
    Biom J 2017 Sep 27. Epub 2017 Sep 27.
    Department of Statistics and Operational Research, University of Granada. Campus Universitario Fuentenueva, 18071, Granada, Spain.
    For surveys of sensitive issues in life sciences, statistical procedures can be used to reduce nonresponse and social desirability response bias. Both of these phenomena provoke nonsampling errors that are difficult to deal with and can seriously flaw the validity of the analyses. The item sum technique (IST) is a very recent indirect questioning method derived from the item count technique that seeks to procure more reliable responses on quantitative items than direct questioning while preserving respondents' anonymity. Read More

    Joint model selection of marginal mean regression and correlation structure for longitudinal data with missing outcome and covariates.
    Biom J 2017 Sep 14. Epub 2017 Sep 14.
    Institute of Statistical Science, Academia Sinica, Taipei, Taiwan, 11529, R.O.C.
    This work develops a joint model selection criterion for simultaneously selecting the marginal mean regression and the correlation/covariance structure in longitudinal data analysis where both the outcome and the covariate variables may be subject to general intermittent patterns of missingness under the missing at random mechanism. The new proposal, termed "joint longitudinal information criterion" (JLIC), is based on the expected quadratic error for assessing model adequacy, and the second-order weighted generalized estimating equation (WGEE) estimation for mean and covariance models. Simulation results reveal that JLIC outperforms existing methods performing model selection for the mean regression and the correlation structure in a two stage and hence separate manner. Read More

    A sequential test for assessing observed agreement between raters.
    Biom J 2017 Sep 12. Epub 2017 Sep 12.
    Department of Information Systems, Statistics and Management Science, University of Alabama, AL, 35487, USA.
    Assessing the agreement between two or more raters is an important topic in medical practice. Existing techniques, which deal with categorical data, are based on contingency tables. This is often an obstacle in practice as we have to wait for a long time to collect the appropriate sample size of subjects to construct the contingency table. Read More

    Bayesian estimation of multivariate normal mixtures with covariate-dependent mixing weights, with an application in antimicrobial resistance monitoring.
    Biom J 2017 Sep 12. Epub 2017 Sep 12.
    Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, BE3590, Diepenbeek, Belgium.
    Bacteria with a reduced susceptibility against antimicrobials pose a major threat to public health. Therefore, large programs have been set up to collect minimum inhibition concentration (MIC) values. These values can be used to monitor the distribution of the nonsusceptible isolates in the general population. Read More

    A comparison of group prediction approaches in longitudinal discriminant analysis.
    Biom J 2017 Aug 21. Epub 2017 Aug 21.
    Department of Biostatistics, University of Liverpool, Liverpool, UK.
    Longitudinal discriminant analysis (LoDA) can be used to classify patients into prognostic groups based on their clinical history, which often involves longitudinal measurements of various clinically relevant markers. Patients' longitudinal data is first modelled using multivariate generalised linear mixed models, allowing markers of different types (e.g. Read More

    Bayesian joint modeling of bivariate longitudinal and competing risks data: An application to study patient-ventilator asynchronies in critical care patients.
    Biom J 2017 Nov 11;59(6):1184-1203. Epub 2017 Aug 11.
    Critical Care Center, Parc Taulí University Hospital, Institut d'Investigació i Innovació Parc Taulí (I3PT), Universitat Autònoma de Barcelona, Sabadell, Spain.
    Mechanical ventilation is a common procedure of life support in intensive care. Patient-ventilator asynchronies (PVAs) occur when the timing of the ventilator cycle is not simultaneous with the timing of the patient respiratory cycle. The association between severity markers and the events death or alive discharge has been acknowledged before, however, little is known about the addition of PVAs data to the analyses. Read More

    Flexible Bayesian additive joint models with an application to type 1 diabetes research.
    Biom J 2017 Nov 10;59(6):1144-1165. Epub 2017 Aug 10.
    Department of Statistics, Ludwig-Maximilians-Universität München, München, Germany.
    The joint modeling of longitudinal and time-to-event data is an important tool of growing popularity to gain insights into the association between a biomarker and an event process. We develop a general framework of flexible additive joint models that allows the specification of a variety of effects, such as smooth nonlinear, time-varying and random effects, in the longitudinal and survival parts of the models. Our extensions are motivated by the investigation of the relationship between fluctuating disease-specific markers, in this case autoantibodies, and the progression to the autoimmune disease type 1 diabetes. Read More

    Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking.
    Biom J 2017 Nov 9;59(6):1261-1276. Epub 2017 Aug 9.
    Department of Biostatistics, Erasmus Medical Center, The Netherlands.
    A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in optimizing medical care. These tests are often performed on a regular basis in order to closely follow the progression of the disease. Read More

    Random walk designs for selecting pool sizes in group testing estimation with small samples.
    Biom J 2017 Nov 9;59(6):1382-1398. Epub 2017 Aug 9.
    Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD, 21250, USA.
    Group testing estimation, which utilizes pooled rather than individual units for testing, has been an ongoing area of research for over six decades. While it is often argued that such methods can yield large savings in terms of resources and/or time, these benefits depend very much on the initial choice of pool sizes. In fact, when poor group sizes are used, the results can be much worse than those obtained using standard techniques. Read More

    Addressing small sample size bias in multiple-biomarker trials: Inclusion of biomarker-negative patients and Firth correction.
    Biom J 2017 Aug 1. Epub 2017 Aug 1.
    Department of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    In recent years, numerous approaches for biomarker-based clinical trials have been developed. One of these developments are multiple-biomarker trials, which aim to investigate multiple biomarkers simultaneously in independent subtrials. For low-prevalence biomarkers, small sample sizes within the subtrials have to be expected, as well as many biomarker-negative patients at the screening stage. Read More

    Prediction accuracy and variable selection for penalized cause-specific hazards models.
    Biom J 2017 Aug 1. Epub 2017 Aug 1.
    Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
    We consider modeling competing risks data in high dimensions using a penalized cause-specific hazards (CSHs) approach. CSHs have conceptual advantages that are useful for analyzing molecular data. First, working on hazards level can further understanding of the underlying biological mechanisms that drive transition hazards. Read More

    Decision-theoretic designs for a series of trials with correlated treatment effects using the Sarmanov multivariate beta-binomial distribution.
    Biom J 2017 Jul 26. Epub 2017 Jul 26.
    Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
    The motivation for the work in this article is the setting in which a number of treatments are available for evaluation in phase II clinical trials and where it may be infeasible to try them concurrently because the intended population is small. This paper introduces an extension of previous work on decision-theoretic designs for a series of phase II trials. The program encompasses a series of sequential phase II trials with interim decision making and a single two-arm phase III trial. Read More

    Characterizing cross-subject spatial interaction patterns in functional magnetic resonance imaging studies: A two-stage point-process model.
    Biom J 2017 Nov 12;59(6):1352-1381. Epub 2017 Jul 12.
    Department of Neurology and Department of Radiology, University of Washington, Seattle, WA, 98185, USA.
    We develop a two-stage spatial point process model that introduces new characterizations of activation patterns in multisubject functional Magnetic Resonance Imaging (fMRI) studies. Conventionally multisubject fMRI methods rely on combining information across subjects one voxel at a time in order to identify locations of peak activation in the brain. The two-stage model that we develop here addresses shortcomings of standard methods by explicitly modeling the spatial structure of functional signals and recognizing that corresponding cross-subject functional signals can be spatially misaligned. Read More

    A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests.
    Biom J 2017 Nov 10;59(6):1317-1338. Epub 2017 Jul 10.
    Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec H3A 1A2, Canada.
    Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Read More

    Multilevel covariance regression with correlated random effects in the mean and variance structure.
    Biom J 2017 Sep 10;59(5):1047-1066. Epub 2017 Jul 10.
    KU Leuven, I-BioStat, Kapucijnenvoer 35, B3000, Leuven, Belgium.
    Multivariate regression methods generally assume a constant covariance matrix for the observations. In case a heteroscedastic model is needed, the parametric and nonparametric covariance regression approaches can be restrictive in the literature. We propose a multilevel regression model for the mean and covariance structure, including random intercepts in both components and allowing for correlation between them. Read More

    A flexible multivariate random effects proportional odds model with application to adverse effects during radiation therapy.
    Biom J 2017 Nov 10;59(6):1339-1351. Epub 2017 Jul 10.
    Institute for Medical Biometry and Statistics, University of Freiburg, Stefan-Meier-Str. 26, 79104, Freiburg, Germany.
    Radiation therapy in patients with head and neck cancer has a toxic effect on mucosa, the soft tissue in and around the mouth. Hence mucositis is a serious common side effect and is a condition characterized by pain and inflammation of the surface of the mucosa. Although the mucosa recovers during breaks of and following the radiotherapy course, the recovery will depend on the type of tissue involved and on its location. Read More

    Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data.
    Biom J 2017 Nov 30;59(6):1301-1316. Epub 2017 Jun 30.
    Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Nijenborgh 9, 9747 AG Groningen, The Netherlands.
    Model-based clustering is a technique widely used to group a collection of units into mutually exclusive groups. There are, however, situations in which an observation could in principle belong to more than one cluster. In the context of next-generation sequencing (NGS) experiments, for example, the signal observed in the data might be produced by two (or more) different biological processes operating together and a gene could participate in both (or all) of them. Read More

    Studying the relationship between a woman's reproductive lifespan and age at menarche using a Bayesian multivariate structured additive distributional regression model.
    Biom J 2017 Nov 29;59(6):1232-1246. Epub 2017 Jun 29.
    Faculty of Medicine, University of Coimbra, Rua Larga, 3004-504, Coimbra, Portugal.
    Studies addressing breast cancer risk factors have been looking at trends relative to age at menarche and menopause. These studies point to a downward trend of age at menarche and an upward trend for age at menopause, meaning an increase of a woman's reproductive lifespan cycle. In addition to studying the effect of the year of birth on the expectation of age at menarche and a woman's reproductive lifespan, it is important to understand how a woman's cohort affects the correlation between these two variables. Read More

    Multivariate binary classification of imbalanced datasets-A case study based on high-dimensional multiplex autoimmune assay data.
    Biom J 2017 Sep 19;59(5):948-966. Epub 2017 Jun 19.
    Department of Mathematical Statistics with Applications in Biometrics, Faculty of Statistics, Technical University Dortmund, Vogelpothsweg 87, 44227, Dortmund, Germany.
    The classification of a population by a specific trait is a major task in medicine, for example when in a diagnostic setting groups of patients with specific diseases are identified, but also when in predictive medicine a group of patients is classified into specific disease severity classes that might profit from different treatments. When the sizes of those subgroups become small, for example in rare diseases, imbalances between the classes are more the rule than the exception and make statistical classification problematic when the error rate of the minority class is high. Many observations are classified as belonging to the majority class, while the error rate of the majority class is low. Read More

    Fast precision estimation in high-dimensional multivariate joint models.
    Biom J 2017 Nov 16;59(6):1221-1231. Epub 2017 Jun 16.
    I-BioStat, KU Leuven, Kapucijnenvoer 35 blok d - box 7001, BE3000 Leuven, Belgium.
    A fast way is proposed based on the multiple outputation idea (Hoffman et al., ; Follmann et al., ) to calculate the precision of parameter estimates for high-dimensional multivariate joint models using a pairwise approach (Fieuws and Verbeke, ; Fieuws et al. Read More

    Stratified exact tests for the weak causal null hypothesis in randomized trials with a binary outcome.
    Biom J 2017 Sep 12;59(5):986-997. Epub 2017 Jun 12.
    Clinical Research Center, Kinki University Hospital, 377-2 Ohno-higashi, Osakasayama, Osaka, Japan.
    Fisher's exact test is commonly used to compare two groups when the outcome is binary in randomized trials. In the context of causal inference, this test explores the sharp causal null hypothesis (i.e. Read More

    Penalized estimation in latent Markov models, with application to monitoring serum calcium levels in end-stage kidney insufficiency.
    Biom J 2017 Sep 8;59(5):1035-1046. Epub 2017 Jun 8.
    Department of Public Health and Infectious Diseases (Sapienza-University of Rome), Piazzale Aldo Moro, 5, 00185, Roma, Italy.
    We introduce a penalized likelihood form for latent Markov models. We motivate its use for biomedical applications where the sample size is in the order of the tens, or at most hundreds, and there are only few repeated measures. The resulting estimates never break down, while spurious solutions are often obtained by maximizing the likelihood itself. Read More

    Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model.
    Biom J 2017 Nov 16;59(6):1277-1300. Epub 2017 May 16.
    Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.
    Dynamic prediction incorporates time-dependent marker information accrued during follow-up to improve personalized survival prediction probabilities. At any follow-up, or "landmark", time, the residual time distribution for an individual, conditional on their updated marker values, can be used to produce a dynamic prediction. To satisfy a consistency condition that links dynamic predictions at different time points, the residual time distribution must follow from a prediction function that models the joint distribution of the marker process and time to failure, such as a joint model. Read More

    A spatially balanced design with probability function proportional to the within sample distance.
    Biom J 2017 Sep 16;59(5):1067-1084. Epub 2017 May 16.
    Istat, Directorate for Methodology and Statistical Process Design, Via Cesare Balbo 16, Rome, IT-00184, Italy.
    The units observed in a biological, agricultural, and environmental survey are often randomly selected from a finite population whose main feature is to be geo-referenced thus its spatial distribution should be used as essential information in designing the sample. In particular our interest is focused on probability samples that are well spread over the population in every dimension which in recent literature are defined as spatially balanced samples. To approach the problem we used the within sample distance as the summary index of the spatial distribution of a random selection criterion. Read More

    A critical evaluation of the current "p-value controversy".
    Biom J 2017 Sep 15;59(5):854-872. Epub 2017 May 15.
    Department of Biostatistics, CIMH Mannheim, Mannheim Medical School of the University of Heidelberg, D-68159, Mannheim, J5, Germany.
    This article has been triggered by the initiative launched in March 2016 by the Board of Directors of the American Statistical Association (ASA) to counteract the current p-value focus of statistical research practices that allegedly "have contributed to a reproducibility crisis in science." It is pointed out that in the very wide field of statistics applied to medicine, many of the problems raised in the ASA statement are not as severe as in the areas the authors may have primarily in mind, although several of them are well-known experts in biostatistics and epidemiology. This is mainly due to the fact that a large proportion of medical research falls under the realm of a well developed body of regulatory rules banning the most frequently occurring misuses of p-values. Read More

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