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    1078 results match your criteria Biometrical Journal [Journal]

    1 OF 22

    Cox model with interval-censored covariate in cohort studies.
    Biom J 2018 May 18. Epub 2018 May 18.
    Department of Neurology, Columbia University, NY, New York, USA.
    In cohort studies the outcome is often time to a particular event, and subjects are followed at regular intervals. Periodic visits may also monitor a secondary irreversible event influencing the event of primary interest, and a significant proportion of subjects develop the secondary event over the period of follow-up. The status of the secondary event serves as a time-varying covariate, but is recorded only at the times of the scheduled visits, generating incomplete time-varying covariates. Read More

    Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample.
    Biom J 2018 May 16. Epub 2018 May 16.
    The Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, 80231, USA.
    Vaccine safety studies are often electronic health record (EHR)-based observational studies. These studies often face significant methodological challenges, including confounding and misclassification of adverse event. Vaccine safety researchers use self-controlled case series (SCCS) study design to handle confounding effect and employ medical chart review to ascertain cases that are identified using EHR data. Read More

    A cure-rate model for Q-learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients.
    Biom J 2018 May 16. Epub 2018 May 16.
    Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.
    Cancers treated by transplantation are often curative, but immunosuppressive drugs are required to prevent and (if needed) to treat graft-versus-host disease. Estimation of an optimal adaptive treatment strategy when treatment at either one of two stages of treatment may lead to a cure has not yet been considered. Using a sample of 9563 patients treated for blood and bone cancers by allogeneic hematopoietic cell transplantation drawn from the Center for Blood and Marrow Transplant Research database, we provide a case study of a novel approach to Q-learning for survival data in the presence of a potentially curative treatment, and demonstrate the results differ substantially from an implementation of Q-learning that fails to account for the cure-rate. Read More

    One-inflation and unobserved heterogeneity in population size estimation by Ryan T. Godwin.
    Biom J 2018 May 11. Epub 2018 May 11.
    Department of Statistics, Middle East Technical University, Ankara, 06800, Turkey.
    In this study, we would like to show that the one-inflated zero-truncated negative binomial (OIZTNB) regression model can be easily implemented in R via built-in functions when we use mean-parameterization feature of negative binomial distribution to build OIZTNB regression model. From the practitioners' point of view, we believe that this approach presents a computationally convenient way for implementation of the OIZTNB regression model. Read More

    Marginalized zero-inflated Poisson models with missing covariates.
    Biom J 2018 May 11. Epub 2018 May 11.
    Department of Statistics, University of Calcutta, Kolkata, India.
    Unlike zero-inflated Poisson regression, marginalized zero-inflated Poisson (MZIP) models for counts with excess zeros provide estimates with direct interpretations for the overall effects of covariates on the marginal mean. In the presence of missing covariates, MZIP and many other count data models are ordinarily fitted using complete case analysis methods due to lack of appropriate statistical methods and software. This article presents an estimation method for MZIP models with missing covariates. Read More

    Multiple testing with discrete data: Proportion of true null hypotheses and two adaptive FDR procedures.
    Biom J 2018 May 11. Epub 2018 May 11.
    Methodology Research, Merck Research Laboratories, North Wales, PA, USA.
    We consider multiple testing with false discovery rate (FDR) control when p values have discrete and heterogeneous null distributions. We propose a new estimator of the proportion of true null hypotheses and demonstrate that it is less upwardly biased than Storey's estimator and two other estimators. The new estimator induces two adaptive procedures, that is, an adaptive Benjamini-Hochberg (BH) procedure and an adaptive Benjamini-Hochberg-Heyse (BHH) procedure. Read More

    Bivariate random-effects meta-analysis models for diagnostic test accuracy studies using arcsine-based transformations.
    Biom J 2018 May 11. Epub 2018 May 11.
    Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada, L8S 4K1.
    Diagnostic or screening tests are widely used in medical fields to classify patients according to their disease status. Several statistical models for meta-analysis of diagnostic test accuracy studies have been developed to synthesize test sensitivity and specificity of a diagnostic test of interest. Because of the correlation between test sensitivity and specificity, modeling the two measures using a bivariate model is recommended. Read More

    Modeling clustered long-term survivors using marginal mixture cure model.
    Biom J 2018 May 7. Epub 2018 May 7.
    Department of Public Health Sciences, Queen's University, Kingston, ON, Canada.
    There is a great deal of recent interests in modeling right-censored clustered survival time data with a possible fraction of cured subjects who are nonsusceptible to the event of interest using marginal mixture cure models. In this paper, we consider a semiparametric marginal mixture cure model for such data and propose to extend an existing generalized estimating equation approach by a new unbiased estimating equation for the regression parameters in the latency part of the model. The large sample properties of the regression effect estimators in both incidence and the latency parts are established. Read More

    A simulation approach for power calculation in large cohort studies based on multistate models.
    Biom J 2018 May 2. Epub 2018 May 2.
    Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Germany.
    Realistic power calculations for large cohort studies and nested case control studies are essential for successfully answering important and complex research questions in epidemiology and clinical medicine. For this, we provide a methodical framework for general realistic power calculations via simulations that we put into practice by means of an R-based template. We consider staggered recruitment and individual hazard rates, competing risks, interaction effects, and the misclassification of covariates. Read More

    Bayesian propensity scores for high-dimensional causal inference: A comparison of drug-eluting to bare-metal coronary stents.
    Biom J 2018 Apr 23. Epub 2018 Apr 23.
    Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.
    High-dimensional data provide many potential confounders that may bolster the plausibility of the ignorability assumption in causal inference problems. Propensity score methods are powerful causal inference tools, which are popular in health care research and are particularly useful for high-dimensional data. Recent interest has surrounded a Bayesian treatment of propensity scores in order to flexibly model the treatment assignment mechanism and summarize posterior quantities while incorporating variance from the treatment model. Read More

    Simultaneous confidence sets for several effective doses.
    Biom J 2018 Apr 3. Epub 2018 Apr 3.
    Mathematical Sciences, University of Southampton, Highfield Campus, University Road, Southampton, SO17 1BJ, UK.
    Construction of simultaneous confidence sets for several effective doses currently relies on inverting the Scheffé type simultaneous confidence band, which is known to be conservative. We develop novel methodology to make the simultaneous coverage closer to its nominal level, for both two-sided and one-sided simultaneous confidence sets. Our approach is shown to be considerably less conservative than the current method, and is illustrated with an example on modeling the effect of smoking status and serum triglyceride level on the probability of the recurrence of a myocardial infarction. Read More

    On the performance of adaptive preprocessing technique in analyzing high-dimensional censored data.
    Biom J 2018 Mar 30. Epub 2018 Mar 30.
    Applied Statistics, Institute of Statistical Research and Training, University of Dhaka, Dhaka, 1000, Bangladesh.
    Preprocessing for high-dimensional censored datasets, such as the microarray data, is generally considered as an important technique to gain further stability by reducing potential noise from the data. When variable selection including inference is carried out with high-dimensional censored data the objective is to obtain a smaller subset of variables and then perform the inferential analysis using model estimates based on the selected subset of variables. This two stage inferential analysis is prone to circularity bias because of the noise that might still remain in the dataset. Read More

    Smooth individual level covariates adjustment in disease mapping.
    Biom J 2018 May 25;60(3):597-615. Epub 2018 Mar 25.
    School of Mathematical and Physical Sciences, University of Technology Sydney, 15 Broadway, Ultimo, NSW, 2007, Australia.
    Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. Read More

    Dynamic prediction of cumulative incidence functions by direct binomial regression.
    Biom J 2018 Mar 25. Epub 2018 Mar 25.
    Radboud University Medical Center, Radboud Institute of Molecular Life Sciences, Nijmegen, The Netherlands.
    In recent years there have been a series of advances in the field of dynamic prediction. Among those is the development of methods for dynamic prediction of the cumulative incidence function in a competing risk setting. These models enable the predictions to be updated as time progresses and more information becomes available, for example when a patient comes back for a follow-up visit after completing a year of treatment, the risk of death, and adverse events may have changed since treatment initiation. Read More

    Relative efficiency of unequal versus equal cluster sizes in cluster randomized trials using generalized estimating equation models.
    Biom J 2018 May 25;60(3):616-638. Epub 2018 Mar 25.
    Department of Surgery, Washington University in Saint Louis (WUSTL), St Louis, Missouri, 63110, USA.
    There is growing interest in conducting cluster randomized trials (CRTs). For simplicity in sample size calculation, the cluster sizes are assumed to be identical across all clusters. However, equal cluster sizes are not guaranteed in practice. Read More

    Incorporating historical information in biosimilar trials: Challenges and a hybrid Bayesian-frequentist approach.
    Biom J 2018 May 13;60(3):564-582. Epub 2018 Mar 13.
    Statistical Methodology, Novartis Pharma AG, 4002, Basel, Switzerland.
    For the approval of biosimilars, it is, in most cases, necessary to conduct large Phase III clinical trials in patients to convince the regulatory authorities that the product is comparable in terms of efficacy and safety to the originator product. As the originator product has already been studied in several trials beforehand, it seems natural to include this historical information into the showing of equivalent efficacy. Since all studies for the regulatory approval of biosimilars are confirmatory studies, it is required that the statistical approach has reasonable frequentist properties, most importantly, that the Type I error rate is controlled-at least in all scenarios that are realistic in practice. Read More

    Mixtures of Berkson and classical covariate measurement error in the linear mixed model: Bias analysis and application to a study on ultrafine particles.
    Biom J 2018 May 13;60(3):480-497. Epub 2018 Mar 13.
    Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
    The ultrafine particle measurements in the Augsburger Umweltstudie, a panel study conducted in Augsburg, Germany, exhibit measurement error from various sources. Measurements of mobile devices show classical possibly individual-specific measurement error; Berkson-type error, which may also vary individually, occurs, if measurements of fixed monitoring stations are used. The combination of fixed site and individual exposure measurements results in a mixture of the two error types. Read More

    Inference from single occasion capture experiments using genetic markers.
    Biom J 2018 May 13;60(3):463-479. Epub 2018 Mar 13.
    School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC, 3010, Australia.
    Accurate estimation of the size of animal populations is an important task in ecological science. Recent advances in the field of molecular genetics researches allow the use of genetic data to estimate the size of a population from a single capture occasion rather than repeated occasions as in the usual capture-recapture experiments. Estimating the population size using genetic data also has sometimes led to estimates that differ markedly from each other and also from classical capture-recapture estimates. Read More

    Identification of causal effects with latent confounding and classical additive errors in treatment.
    Biom J 2018 May 13;60(3):498-515. Epub 2018 Mar 13.
    Beijing International Center for Mathematical Research, Peking University, Beijing, 100871, China.
    In this paper, we discuss the identifiability and estimation of causal effects of a continuous treatment on a binary response when the treatment is measured with errors and there exists a latent categorical confounder associated with both treatment and response. Under some widely used parametric models, we first discuss the identifiability of the causal effects and then propose an approach for estimation and inference. Our approach can eliminate the biases induced by latent confounding and measurement errors by using only a single instrumental variable. Read More

    A cautionary note on Bayesian estimation of population size by removal sampling with diffuse priors.
    Biom J 2018 May 12;60(3):450-462. Epub 2018 Mar 12.
    Université Clermont Auvergne, CNRS, Laboratoire de Mathématiques Blaise Pascal, F-63000 CLERMONT-FERRAND, FRANCE.
    We consider the problem of estimating a population size by removal sampling when the sampling rate is unknown. Bayesian methods are now widespread and allow to include prior knowledge in the analysis. However, we show that Bayes estimates based on default improper priors lead to improper posteriors or infinite estimates. Read More

    Hypothesis tests for stratified mark-specific proportional hazards models with missing covariates, with application to HIV vaccine efficacy trials.
    Biom J 2018 May 28;60(3):516-536. Epub 2018 Feb 28.
    Department of Biostatistics, University of Washington, and Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.
    This article develops hypothesis testing procedures for the stratified mark-specific proportional hazards model with missing covariates where the baseline functions may vary with strata. The mark-specific proportional hazards model has been studied to evaluate mark-specific relative risks where the mark is the genetic distance of an infecting HIV sequence to an HIV sequence represented inside the vaccine. This research is motivated by analyzing the RV144 phase 3 HIV vaccine efficacy trial, to understand associations of immune response biomarkers on the mark-specific hazard of HIV infection, where the biomarkers are sampled via a two-phase sampling nested case-control design. Read More

    Bayesian nonparametric inference for panel count data with an informative observation process.
    Biom J 2018 May 22;60(3):583-596. Epub 2018 Feb 22.
    Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital, Cincinnati, OH, 45229, USA.
    In this paper, the panel count data analysis for recurrent events is considered. Such analysis is useful for studying tumor or infection recurrences in both clinical trial and observational studies. A bivariate Gaussian Cox process model is proposed to jointly model the observation process and the recurrent event process. Read More

    Evaluating the effects of rater and subject factors on measures of association.
    Biom J 2018 May 19;60(3):639-656. Epub 2018 Jan 19.
    Department of Statistics, University of South Carolina, Columbia, SC, 29205, USA.
    Large-scale agreement studies are becoming increasingly common in medical settings to gain better insight into discrepancies often observed between experts' classifications. Ordered categorical scales are routinely used to classify subjects' disease and health conditions. Summary measures such as Cohen's weighted kappa are popular approaches for reporting levels of association for pairs of raters' ordinal classifications. Read More

    Small area estimation of proportions with different levels of auxiliary data.
    Biom J 2018 Mar 19;60(2):395-415. Epub 2018 Jan 19.
    ICAR-Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi, 110012, India.
    Binary data are often of interest in many small areas of applications. The use of standard small area estimation methods based on linear mixed models becomes problematic for such data. An empirical plug-in predictor (EPP) under a unit-level generalized linear mixed model with logit link function is often used for the estimation of a small area proportion. Read More

    Reconstruction of molecular network evolution from cross-sectional omics data.
    Biom J 2018 May 10;60(3):547-563. Epub 2018 Jan 10.
    Department of Mathematics, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands.
    Cross-sectional studies may shed light on the evolution of a disease like cancer through the comparison of patient traits among disease stages. This problem is especially challenging when a gene-gene interaction network needs to be reconstructed from omics data, and, in addition, the patients of each stage need not form a homogeneous group. Here, the problem is operationalized as the estimation of stage-wise mixtures of Gaussian graphical models (GGMs) from high-dimensional data. Read More

    Variable selection - A review and recommendations for the practicing statistician.
    Biom J 2018 May 2;60(3):431-449. Epub 2018 Jan 2.
    Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, 1090, Austria.
    Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well-established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. Read More

    Modeling time-varying exposure using inverse probability of treatment weights.
    Biom J 2018 Mar 27;60(2):323-332. Epub 2017 Dec 27.
    INSERM U1153, Statistic and Epidemiologic Research Center Sorbonne Paris Cité (CRESS), ECSTRA Team, Saint-Louis Hospital, Paris, France.
    For estimating the causal effect of treatment exposure on the occurrence of adverse events, inverse probability weights (IPW) can be used in marginal structural models to correct for time-dependent confounding. The R package ipw allows IPW estimation by modeling the relationship between the exposure and confounders via several regression models, among which is the Cox model. For right-censored data and time-dependent exposures such as treatment switches, the ipw package allows a single switch, assuming that patients are treated once and for all. Read More

    Multiple-rater kappas for binary data: Models and interpretation.
    Biom J 2018 Mar 27;60(2):381-394. Epub 2017 Dec 27.
    German Cancer Research Center (DKFZ), Department of Biostatistics, Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany.
    Interrater agreement on binary measurements with more than two raters is often assessed using Fleiss' κ, which is known to be difficult to interpret. In situations where the same raters rate all items, however, the far less known κ suggested by Conger, Hubert, and Schouten is more appropriate. We try to support the interpretation of these characteristics by investigating various models or scenarios of rating. Read More

    Local influence diagnostics for hierarchical finite-mixture random-effects models.
    Biom J 2018 Mar 19;60(2):369-380. Epub 2017 Dec 19.
    I-BioStat, Hasselt University, B-3500, Hasselt, Belgium.
    The main objective of this paper is to evaluate the influence of individual subjects exerted on a random-effects model for repeated measures, where the random effects follow a mixture distribution. The diagnostic tool is based on local influence with perturbation scheme that explicitly targets influences resulting from perturbing the mixture component probabilities. Bruckers, Molenberghs, Verbeke, and Geys (2016) considered a similar model, but focused on influences stemming from perturbing a subject's likelihood contributions as a whole. Read More

    Variance component analysis to assess protein quantification in biomarker discovery. Application to MALDI-TOF mass spectrometry.
    Biom J 2018 Mar 12;60(2):262-274. Epub 2017 Dec 12.
    Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, Lyon, France.
    Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. Read More

    Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information.
    Biom J 2018 May 5;60(3):537-546. Epub 2017 Dec 5.
    Division of Clinical Research, The First Hospital of Jilin University, Changchun, 130021, China.
    Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. Read More

    Estimating the DINA model parameters using the No-U-Turn Sampler.
    Biom J 2018 Mar 1;60(2):352-368. Epub 2017 Dec 1.
    Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo. Av. Trabalhador São Carlense, 400., 13566-590, São Carlos, SP, Brasil.
    The deterministic inputs, noisy, "and" gate (DINA) model is a popular cognitive diagnosis model (CDM) in psychology and psychometrics used to identify test takers' profiles with respect to a set of latent attributes or skills. In this work, we propose an estimation method for the DINA model with the No-U-Turn Sampler (NUTS) algorithm, an extension to Hamiltonian Monte Carlo (HMC) method. We conduct a simulation study in order to evaluate the parameter recovery and efficiency of this new Markov chain Monte Carlo method and to compare it with two other Bayesian methods, the Metropolis Hastings and Gibbs sampling algorithms, and with a frequentist method, using the Expectation-Maximization (EM) algorithm. Read More

    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 2018 Jan 8;60(1):115-127. 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 2018 Jan 7;60(1):207-215. 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 2018 Jan 7;60(1):146-154. 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 2018 Jan 27;60(1):196-206. 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 2018 Jan 27;60(1):100-114. 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 2018 Jan 26;60(1):79-99. 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 2018 Jan 25;60(1):49-65. 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 2018 Jan 25;60(1):34-48. 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 2018 Jan 25;60(1):66-78. 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 2018 Jan 13;60(1):174-195. 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 2018 Mar 12;60(2):246-261. 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 2018 Mar 9;60(2):333-351. 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

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