1,178 results match your criteria Biometrical Journal [Journal]


Assessment of local influence for the analysis of agreement.

Biom J 2019 Feb 15. Epub 2019 Feb 15.

Departamento de Matemática, Universidad Técnica Federico Santa María, Valparaíso, Chile.

The concordance correlation coefficient (CCC) and the probability of agreement (PA) are two frequently used measures for evaluating the degree of agreement between measurements generated by two different methods. In this paper, we consider the CCC and the PA using the bivariate normal distribution for modeling the observations obtained by two measurement methods. The main aim of this paper is to develop diagnostic tools for the detection of those observations that are influential on the maximum likelihood estimators of the CCC and the PA using the local influence methodology but not based on the likelihood displacement. Read More

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http://dx.doi.org/10.1002/bimj.201800124DOI Listing
February 2019

Mixture and nonmixture cure fraction models assuming discrete lifetimes: Application to a pelvic sarcoma dataset.

Biom J 2019 Feb 14. Epub 2019 Feb 14.

Medical School, Universidade de São Paulo, Ribeirão Preto, SP, Brasil.

Different cure fraction models have been used in the analysis of lifetime data in presence of cured patients. This paper considers mixture and nonmixture models based on discrete Weibull distribution to model recurrent event data in presence of a cure fraction. The novelty of this study is the use of a discrete lifetime distribution in place of usual existing continuous lifetime distributions for lifetime data in presence of cured fraction, censored data, and covariates. Read More

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http://dx.doi.org/10.1002/bimj.201800030DOI Listing
February 2019

Marginal false discovery rate control for likelihood-based penalized regression models.

Biom J 2019 Feb 11. Epub 2019 Feb 11.

Department of Biostatistics, University of Iowa, Iowa City, IA, USA.

The popularity of penalized regression in high-dimensional data analysis has led to a demand for new inferential tools for these models. False discovery rate control is widely used in high-dimensional hypothesis testing, but has only recently been considered in the context of penalized regression. Almost all of this work, however, has focused on lasso-penalized linear regression. Read More

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http://dx.doi.org/10.1002/bimj.201800138DOI Listing
February 2019

Testing random effects in linear mixed-effects models with serially correlated errors.

Biom J 2019 Feb 5. Epub 2019 Feb 5.

Department of Statistics, Faculty of Science, University of Qom, Qom, Iran.

In linear mixed-effects models, random effects are used to capture the heterogeneity and variability between individuals due to unmeasured covariates or unknown biological differences. Testing for the need of random effects is a nonstandard problem because it requires testing on the boundary of parameter space where the asymptotic chi-squared distribution of the classical tests such as likelihood ratio and score tests is incorrect. In the literature several tests have been proposed to overcome this difficulty, however all of these tests rely on the restrictive assumption of i. Read More

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http://dx.doi.org/10.1002/bimj.201700203DOI Listing
February 2019

Interim analysis incorporating short- and long-term binary endpoints.

Biom J 2019 Jan 29. Epub 2019 Jan 29.

Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

Designs incorporating more than one endpoint have become popular in drug development. One of such designs allows for incorporation of short-term information in an interim analysis if the long-term primary endpoint has not been yet observed for some of the patients. At first we consider a two-stage design with binary endpoints allowing for futility stopping only based on conditional power under both fixed and observed effects. Read More

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http://dx.doi.org/10.1002/bimj.201700281DOI Listing
January 2019

Powerful testing via hierarchical linkage disequilibrium in haplotype association studies.

Biom J 2019 Jan 28. Epub 2019 Jan 28.

Department of Biomedical Data Sciences, Section Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.

Marginal tests based on individual SNPs are routinely used in genetic association studies. Studies have shown that haplotype-based methods may provide more power in disease mapping than methods based on single markers when, for example, multiple disease-susceptibility variants occur within the same gene. A limitation of haplotype-based methods is that the number of parameters increases exponentially with the number of SNPs, inducing a commensurate increase in the degrees of freedom and weakening the power to detect associations. Read More

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http://doi.wiley.com/10.1002/bimj.201800053
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http://dx.doi.org/10.1002/bimj.201800053DOI Listing
January 2019
2 Reads

Analysis of cause of death: Competing risks or progressive illness-death model?

Biom J 2019 Jan 25. Epub 2019 Jan 25.

Department of Epidemiology, Medical Statistics and Decision Making, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.

The analysis of cause of death is increasingly becoming a topic in oncology. It is usually distinguished between disease-related and disease-unrelated death. A frequently used approach is to define death as disease-related when a progression to advanced phases has occurred before, otherwise as disease-unrelated. Read More

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http://doi.wiley.com/10.1002/bimj.201700238
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http://dx.doi.org/10.1002/bimj.201700238DOI Listing
January 2019
4 Reads

An efficient sample size adaptation strategy with adjustment of randomization ratio.

Biom J 2019 Jan 16. Epub 2019 Jan 16.

AbbVie Inc., North Chicago, IL, USA.

In clinical trials, sample size reestimation is a useful strategy for mitigating the risk of uncertainty in design assumptions and ensuring sufficient power for the final analysis. In particular, sample size reestimation based on unblinded interim effect size can often lead to sample size increase, and statistical adjustment is usually needed for the final analysis to ensure that type I error rate is appropriately controlled. In current literature, sample size reestimation and corresponding type I error control are discussed in the context of maintaining the original randomization ratio across treatment groups, which we refer to as "proportional increase. Read More

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http://dx.doi.org/10.1002/bimj.201800119DOI Listing
January 2019
1 Read

Estimation of log-odds ratio from group testing data using Firth correction.

Biom J 2019 Jan 15. Epub 2019 Jan 15.

Indian Institute of Management, Ahmedabad, Vastrapur, Ahmedabad, India.

We consider the estimation of the prevalence of a rare disease, and the log-odds ratio for two specified groups of individuals from group testing data. For a low-prevalence disease, the maximum likelihood estimate of the log-odds ratio is severely biased. However, Firth correction to the score function leads to a considerable improvement of the estimator. Read More

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http://dx.doi.org/10.1002/bimj.201800125DOI Listing
January 2019
1 Read

Editorial for the MCP 2017 Special Issue.

Biom J 2019 Jan;61(1)

Guest Editors for the MCP 2017 Special Issue.

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http://dx.doi.org/10.1002/bimj.201800364DOI Listing
January 2019
1 Read

Improved power of familywise error rate procedures for discrete data under dependency.

Biom J 2019 Jan;61(1):101-114

Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, Pennsylvania, USA.

In many applications where it is necessary to test multiple hypotheses simultaneously, the data encountered are discrete. In such cases, it is important for multiplicity adjustment to take into account the discreteness of the distributions of the p-values, to assure that the procedure is not overly conservative. In this paper, we review some known multiple testing procedures for discrete data that control the familywise error rate, the probability of making any false rejection. Read More

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http://dx.doi.org/10.1002/bimj.201700332DOI Listing
January 2019
1 Read

Random main effects of treatment: A case study with a network meta-analysis.

Biom J 2019 Jan 9. Epub 2019 Jan 9.

L'Oréal Research and Innovation, Clichy, France.

If the number of treatments in a network meta-analysis is large, it may be possible and useful to model the main effect of treatment as random, that is to say as random realizations from a normal distribution of possible treatment effects. This then constitutes a third sort of random effect that may be considered in connection with such analyses. The first and most common models treatment-by-trial interaction as being random and the second, rather rarer, models the main effects of trial as being random and thus permits the recovery of intertrial information. Read More

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http://dx.doi.org/10.1002/bimj.201700265DOI Listing
January 2019
1 Read

On the interpretation of the hazard ratio in Cox regression.

Biom J 2019 Jan 9. Epub 2019 Jan 9.

Department of Biostatistics, University of Copenhagen, Copenhagen K, Denmark.

We argue that the term "relative risk" should not be used as a synonym for "hazard ratio" and encourage to use the probabilistic index as an alternative effect measure for Cox regression. The probabilistic index is the probability that the event time of an exposed or treated subject exceeds the event time of an unexposed or untreated subject conditional on the other covariates. It arises as a well known and simple transformation of the hazard ratio and nicely reveals the interpretational limitations. Read More

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http://dx.doi.org/10.1002/bimj.201800255DOI Listing
January 2019
1 Read

K-Sample comparisons using propensity analysis.

Biom J 2019 Jan 7. Epub 2019 Jan 7.

Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.

In this paper, we investigate K-group comparisons on survival endpoints for observational studies. In clinical databases for observational studies, treatment for patients are chosen with probabilities varying depending on their baseline characteristics. This often results in noncomparable treatment groups because of imbalance in baseline characteristics of patients among treatment groups. Read More

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http://dx.doi.org/10.1002/bimj.201800049DOI Listing
January 2019
1 Read
0.945 Impact Factor

Mid-P confidence intervals for group testing based on the total number of positive groups.

Biom J 2019 Jan 4. Epub 2019 Jan 4.

Department of Biostatistics, University of Alabama at Birmingham, Alabama, USA.

In the estimation of proportions by group testing, unequal sized groups results in an ambiguous ordering of the sample space, which complicates the construction of exact confidence intervals. The total number of positive groups is shown to be a suitable statistic for ordering outcomes, provided its ties are broken by the MLE. We propose an interval estimation method based on this quantity, with a mid-P correction. Read More

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http://doi.wiley.com/10.1002/bimj.201700190
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http://dx.doi.org/10.1002/bimj.201700190DOI Listing
January 2019
7 Reads

The hierarchical metaregression approach and learning from clinical evidence.

Biom J 2019 Jan 2. Epub 2019 Jan 2.

Coordination Center for Clinical Trials, Düsseldorf University Hospital, Moorenstr, Düsseldorf, Germany.

The hierarchical metaregression (HMR) approach is a multiparameter Bayesian approach for meta-analysis, which generalizes the standard mixed effects models by explicitly modeling the data collection process in the meta-analysis. The HMR allows to investigate the potential external validity of experimental results as well as to assess the internal validity of the studies included in a systematic review. The HMR automatically identifies studies presenting conflicting evidence and it downweights their influence in the meta-analysis. Read More

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http://doi.wiley.com/10.1002/bimj.201700266
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http://dx.doi.org/10.1002/bimj.201700266DOI Listing
January 2019
5 Reads

Goodness-of-fit tests for disorder detection in NGS experiments.

Biom J 2018 Dec 27. Epub 2018 Dec 27.

Department of Statistics and Operations Research, SiDOR Research Group & CINBIO, University of Vigo, Vigo, Pontevedra, Spain.

Next-generation sequencing (NGS) experiments are often performed in biomedical research nowadays, leading to methodological challenges related to the high-dimensional and complex nature of the recorded data. In this work we review some of the issues that arise in disorder detection from NGS experiments, that is, when the focus is the detection of deletion and duplication disorders for homozygosity and heterozygosity in DNA sequencing. A statistical model to cope with guanine/cytosine bias and phasing and prephasing phenomena at base level is proposed, and a goodness-of-fit procedure for disorder detection is derived. Read More

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http://doi.wiley.com/10.1002/bimj.201700284
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http://dx.doi.org/10.1002/bimj.201700284DOI Listing
December 2018
6 Reads

The changing landscape of data monitoring committees-Perspectives from regulators, members, and sponsors.

Biom J 2018 Dec 27. Epub 2018 Dec 27.

ACI Clinical, Bala Cynwyd, PA, USA.

Data Monitoring Committees (DMCs) are an integral part of clinical drug development. Their use has evolved along with changing study designs and regulatory expectations, which has associated statistical and ethical implications. Although there is guidance from the different regulatory agencies, there are opportunities to bring more consistency to address practical issues of establishing and operating a DMC. Read More

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http://dx.doi.org/10.1002/bimj.201700307DOI Listing
December 2018
1 Read

BMA-Mod: A Bayesian model averaging strategy for determining dose-response relationships in the presence of model uncertainty.

Authors:
A Lawrence Gould

Biom J 2018 Dec 19. Epub 2018 Dec 19.

Merck & Co. Inc., Upper Gwynedd, Pennsylvania, USA.

Successful pharmaceutical drug development requires finding correct doses. The issues that conventional dose-response analyses consider, namely whether responses are related to doses, which doses have responses differing from a control dose response, the functional form of a dose-response relationship, and the dose(s) to carry forward, do not need to be addressed simultaneously. Determining if a dose-response relationship exists, regardless of its functional form, and then identifying a range of doses to study further may be a more efficient strategy. Read More

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http://dx.doi.org/10.1002/bimj.201700211DOI Listing
December 2018
1 Read

Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox model.

Biom J 2018 Dec 13. Epub 2018 Dec 13.

Departarment d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya/BARCELONATECH, Barcelona, Spain.

We present a method to fit a mixed effects Cox model with interval-censored data. Our proposal is based on a multiple imputation approach that uses the truncated Weibull distribution to replace the interval-censored data by imputed survival times and then uses established mixed effects Cox methods for right-censored data. Interval-censored data were encountered in a database corresponding to a recompilation of retrospective data from eight analytical treatment interruption (ATI) studies in 158 human immunodeficiency virus (HIV) positive combination antiretroviral treatment (cART) suppressed individuals. Read More

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http://dx.doi.org/10.1002/bimj.201700291DOI Listing
December 2018
2 Reads

Testing strategy in phase 3 trials with multiple doses.

Biom J 2019 Jan 12;61(1):115-125. Epub 2018 Dec 12.

Great Abington, Cambridge, UK.

In this paper, we consider multiplicity testing approaches mainly for phase 3 trials with two doses. We review a few available approaches and propose some new ones. The doses selected for phase 3 usually have the same or a similar efficacy profile, so they have some degree of consistency in efficacy. Read More

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http://dx.doi.org/10.1002/bimj.201700312DOI Listing
January 2019
1 Read

Tuning model parameters in class-imbalanced learning with precision-recall curve.

Biom J 2018 Dec 12. Epub 2018 Dec 12.

School of Mathematics, The University of Manchester, Manchester, UK.

An issue for class-imbalanced learning is what assessment metric should be employed. So far, precision-recall curve (PRC) as a metric is rarely used in practice as compared with its alternative of receiver operating characteristic (ROC). This study investigates the performance of PRC as the evaluating criterion to address the class-imbalanced data and focuses on the comparison of PRC with ROC. Read More

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http://dx.doi.org/10.1002/bimj.201800148DOI Listing
December 2018
1 Read

Stochastic search variable selection based on two mixture components and continuous-scale weighting.

Biom J 2018 Dec 10. Epub 2018 Dec 10.

Department of Mathematical Sciences and Biocenter Oulu, University of Oulu, Oulu, Finland.

Stochastic search variable selection (SSVS) is a Bayesian variable selection method that employs covariate-specific discrete indicator variables to select which covariates (e.g., molecular markers) are included in or excluded from the model. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.2018001
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http://dx.doi.org/10.1002/bimj.201800118DOI Listing
December 2018
3 Reads

Estimating marginal proportions and intraclass correlations with clustered binary data.

Biom J 2018 Dec 11. Epub 2018 Dec 11.

Biostatistics, Department of Basic Clinical Practice, University of Barcelona, Barcelona, Spain.

A logistic regression with random effects model is commonly applied to analyze clustered binary data, and every cluster is assumed to have a different proportion of success. However, it could be of interest to obtain the proportion of success over clusters (i.e. Read More

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http://dx.doi.org/10.1002/bimj.201700230DOI Listing
December 2018
3 Reads

Maximum likelihood estimation of generalized linear models for adaptive designs: Applications and asymptotics.

Biom J 2018 Dec 10. Epub 2018 Dec 10.

Department of Mathematics and Statistics, Memorial University, St. John's, NL, Canada.

Due to increasing discoveries of biomarkers and observed diversity among patients, there is growing interest in personalized medicine for the purpose of increasing the well-being of patients (ethics) and extending human life. In fact, these biomarkers and observed heterogeneity among patients are useful covariates that can be used to achieve the ethical goals of clinical trials and improving the efficiency of statistical inference. Covariate-adjusted response-adaptive (CARA) design was developed to use information in such covariates in randomization to maximize the well-being of participating patients as well as increase the efficiency of statistical inference at the end of a clinical trial. Read More

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http://dx.doi.org/10.1002/bimj.201800181DOI Listing
December 2018
1 Read

Multiset sparse redundancy analysis for high-dimensional omics data.

Biom J 2018 Dec 3. Epub 2018 Dec 3.

Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, The Netherlands.

Redundancy Analysis (RDA) is a well-known method used to describe the directional relationship between related data sets. Recently, we proposed sparse Redundancy Analysis (sRDA) for high-dimensional genomic data analysis to find explanatory variables that explain the most variance of the response variables. As more and more biomolecular data become available from different biological levels, such as genotypic and phenotypic data from different omics domains, a natural research direction is to apply an integrated analysis approach in order to explore the underlying biological mechanism of certain phenotypes of the given organism. Read More

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http://doi.wiley.com/10.1002/bimj.201700248
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http://dx.doi.org/10.1002/bimj.201700248DOI Listing
December 2018
2 Reads

Bayesian hierarchical classification and information sharing for clinical trials with subgroups and binary outcomes.

Authors:
Nan Chen J Jack Lee

Biom J 2018 Dec 3. Epub 2018 Dec 3.

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Bayesian hierarchical models have been applied in clinical trials to allow for information sharing across subgroups. Traditional Bayesian hierarchical models do not have subgroup classifications; thus, information is shared across all subgroups. When the difference between subgroups is large, it suggests that the subgroups belong to different clusters. Read More

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http://dx.doi.org/10.1002/bimj.201700275DOI Listing
December 2018
2 Reads

Semi-parametric analysis of overdispersed count and metric data with varying follow-up times: Asymptotic theory and small sample approximations.

Biom J 2018 Dec 5. Epub 2018 Dec 5.

Institute of Statistics, Ulm University, Ulm, Germany.

Count data are common endpoints in clinical trials, for example magnetic resonance imaging lesion counts in multiple sclerosis. They often exhibit high levels of overdispersion, that is variances are larger than the means. Inference is regularly based on negative binomial regression along with maximum-likelihood estimators. Read More

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http://dx.doi.org/10.1002/bimj.201800027DOI Listing
December 2018
1 Read

A beta-binomial mixed-effects model approach for analysing longitudinal discrete and bounded outcomes.

Biom J 2018 Nov 27. Epub 2018 Nov 27.

Basque Center for Applied Mathematics, Bilbao, Spain.

Patient-reported outcomes (PROs) are currently being increasingly used as primary outcome measures in observational and experimental studies since they inform clinicians and researchers about the health-status of patients and generate data to facilitate improved care. PROs usually appear as discrete and bounded with U, J, or inverse J shapes, and hence, exponential family members offer inadequate distributional fits. The beta-binomial distribution has been proposed in the literature to fit PROs. Read More

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http://dx.doi.org/10.1002/bimj.201700251DOI Listing
November 2018
1 Read

A Bayesian joint model of recurrent events and a terminal event.

Biom J 2019 Jan 26;61(1):187-202. Epub 2018 Nov 26.

Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennslyvania, USA.

Recurrent events could be stopped by a terminal event, which commonly occurs in biomedical and clinical studies. In this situation, dependent censoring is encountered because of potential dependence between these two event processes, leading to invalid inference if analyzing recurrent events alone. The joint frailty model is one of the widely used approaches to jointly model these two processes by sharing the same frailty term. Read More

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http://doi.wiley.com/10.1002/bimj.201700326
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http://dx.doi.org/10.1002/bimj.201700326DOI Listing
January 2019
9 Reads

Penalized likelihood and multiple testing.

Biom J 2019 Jan 26;61(1):62-72. Epub 2018 Nov 26.

Department of Statistics and Biostatistics, Rutgers University, Hill Center, Piscataway, New Jersey, USA.

The classical multiple testing model remains an important practical area of statistics with new approaches still being developed. In this paper we develop a new multiple testing procedure inspired by a method sometimes used in a problem with a different focus. Namely, the inference after model selection problem. Read More

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http://dx.doi.org/10.1002/bimj.201700196DOI Listing
January 2019
2 Reads

Semiparametric transformation models for interval-censored data in the presence of a cure fraction.

Biom J 2019 Jan 25;61(1):203-215. Epub 2018 Nov 25.

Institute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan.

Mixed case interval-censored data arise when the event of interest is known only to occur within an interval induced by a sequence of random examination times. Such data are commonly encountered in disease research with longitudinal follow-up. Furthermore, the medical treatment has progressed over the last decade with an increasing proportion of patients being cured for many types of diseases. Read More

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http://doi.wiley.com/10.1002/bimj.201700304
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http://dx.doi.org/10.1002/bimj.201700304DOI Listing
January 2019
10 Reads

Adjusting for selection bias in assessing treatment effect estimates from multiple subgroups.

Authors:
Ekkehard Glimm

Biom J 2019 Jan 25;61(1):216-229. Epub 2018 Nov 25.

Novartis Pharma AG, Novartis Campus, Basel, Switzerland.

This paper discusses a number of methods for adjusting treatment effect estimates in clinical trials where differential effects in several subpopulations are suspected. In such situations, the estimates from the most extreme subpopulation are often overinterpreted. The paper focusses on the construction of simultaneous confidence intervals intended to provide a more realistic assessment regarding the uncertainty around these extreme results. Read More

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http://dx.doi.org/10.1002/bimj.201800097DOI Listing
January 2019
1 Read

Confidence regions for treatment effects in subgroups in biomarker stratified designs.

Biom J 2019 Jan 25;61(1):27-39. Epub 2018 Nov 25.

Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.

Subgroup analysis has important applications in the analysis of controlled clinical trials. Sometimes the result of the overall group fails to demonstrate that the new treatment is better than the control therapy, but for a subgroup of patients, the treatment benefit may exist; or sometimes, the new treatment is better for the overall group but not for a subgroup. Hence we are interested in constructing a simultaneous confidence interval for the difference of the treatment effects in a subgroup and the overall group. Read More

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http://doi.wiley.com/10.1002/bimj.201700303
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http://dx.doi.org/10.1002/bimj.201700303DOI Listing
January 2019
9 Reads

Estimation in the progressive illness-death model: A nonexhaustive review.

Biom J 2018 Nov 20. Epub 2018 Nov 20.

Centre of Molecular and Environmental Biology and Department of Mathematics and Applications, University of Minho, Campus de Azurem, Guimarães, Portugal.

Multistate models can be successfully used for describing complex event history data, for example, describing stages in the disease progression of a patient. The so-called "illness-death" model plays a central role in the theory and practice of these models. Many time-to-event datasets from medical studies with multiple end points can be reduced to this generic structure. Read More

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http://dx.doi.org/10.1002/bimj.201700200DOI Listing
November 2018
12 Reads

A note on tests for relevant differences with extremely large sample sizes.

Biom J 2019 Jan 11;61(1):162-165. Epub 2018 Nov 11.

Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Universite Catholique de Louvain, Louvain-la-Neuve, Belgium.

A well-known problem in classical two-tailed hypothesis testing is that P-values go to zero when the sample size goes to infinity, irrespectively of the effect size. This pitfall can make the testing of data consisting of large sample sizes potentially unreliable. In this note, we propose to test for relevant differences to overcome this issue. Read More

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http://doi.wiley.com/10.1002/bimj.201800195
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http://dx.doi.org/10.1002/bimj.201800195DOI Listing
January 2019
12 Reads

Optimal promising zone designs.

Biom J 2018 Nov 8. Epub 2018 Nov 8.

Cytel Corportation, Cambridge, Massachusetts, USA.

Clinical trials with adaptive sample size reassessment based on an unblinded analysis of interim results are perhaps the most popular class of adaptive designs (see Elsäßer et al., 2007). Such trials are typically designed by prespecifying a zone for the interim test statistic, termed the promising zone, along with a decision rule for increasing the sample size within that zone. Read More

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http://dx.doi.org/10.1002/bimj.201700308DOI Listing
November 2018
1 Read

A Bayesian decision analysis approach to assess voice disorder risks by using acoustic features.

Biom J 2018 Nov 8. Epub 2018 Nov 8.

Department of Nursing, Faculty of Nursing, University of Extremadura, Mérida, Spain.

Vocal fold nodules are recognized as an occupational disease for all collective of workers performing activities for which maintained and continued use of voice is required. Computer-aided systems based on features extracted from voice recordings have been considered as potential noninvasive and low cost tools to diagnose some voice-related diseases. A Bayesian decision analysis approach has been proposed to classify university lectures in three levels of risk: low, medium, and high, based on the information provided by acoustic features extracted from healthy controls and people suffering from vocal fold nodules. Read More

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http://doi.wiley.com/10.1002/bimj.201700233
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http://dx.doi.org/10.1002/bimj.201700233DOI Listing
November 2018
8 Reads
0.945 Impact Factor

Construction of confidence intervals for the maximum of the Youden index and the corresponding cutoff point of a continuous biomarker.

Biom J 2019 Jan 8;61(1):138-156. Epub 2018 Nov 8.

Department of Statistics, University of Haifa, Haifa, Israel.

Evaluation of the overall accuracy of biomarkers might be based on average measures of the sensitivity for all possible specificities -and vice versa- or equivalently the area under the receiver operating characteristic (ROC) curve that is typically used in such settings. In practice clinicians are in need of a cutoff point to determine whether intervention is required after establishing the utility of a continuous biomarker. The Youden index can serve both purposes as an overall index of a biomarker's accuracy, that also corresponds to an optimal, in terms of maximizing the Youden index, cutoff point that in turn can be utilized for decision making. Read More

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http://doi.wiley.com/10.1002/bimj.201700107
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http://dx.doi.org/10.1002/bimj.201700107DOI Listing
January 2019
9 Reads
0.945 Impact Factor

An alternative derivation of Harville's restricted log likelihood function for variance component estimation.

Authors:
Shizhong Xu

Biom J 2019 Jan 2;61(1):157-161. Epub 2018 Nov 2.

Department of Botany and Plant Sciences, University of California, Riverside, CA, USA.

Estimation of variance components in linear mixed models is important in clinical trial and longitudinal data analysis. It is also important in animal and plant breeding for accurately partitioning total phenotypic variance into genetic and environmental variances. Restricted maximum likelihood (REML) method is often preferred over the maximum likelihood (ML) method for variance component estimation because REML takes into account the lost degree of freedom resulting from estimating the fixed effects. Read More

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http://dx.doi.org/10.1002/bimj.201800319DOI Listing
January 2019
8 Reads

A population-averaged approach to diagnostic test meta-analysis.

Biom J 2019 Jan 29;61(1):126-137. Epub 2018 Oct 29.

Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.

The meta-analysis of diagnostic accuracy studies is often of interest in screening programs for many diseases. The typical summary statistics for studies chosen for a diagnostic accuracy meta-analysis are often two dimensional: sensitivities and specificities. The common statistical analysis approach for the meta-analysis of diagnostic studies is based on the bivariate generalized linear-mixed model (BGLMM), which has study-specific interpretations. Read More

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http://doi.wiley.com/10.1002/bimj.201700187
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http://dx.doi.org/10.1002/bimj.201700187DOI Listing
January 2019
10 Reads

A comparison of model selection methods for prediction in the presence of multiply imputed data.

Biom J 2018 Oct 23. Epub 2018 Oct 23.

Biostatistics group, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.

Many approaches for variable selection with multiply imputed data in the development of a prognostic model have been proposed. However, no method prevails as uniformly best. We conducted a simulation study with a binary outcome and a logistic regression model to compare two classes of variable selection methods in the presence of MI data: (I) Model selection on bootstrap data, using backward elimination based on AIC or lasso, and fit the final model based on the most frequently (e. Read More

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http://doi.wiley.com/10.1002/bimj.201700232
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http://dx.doi.org/10.1002/bimj.201700232DOI Listing
October 2018
13 Reads

Correct and logical inference on efficacy in subgroups and their mixture for binary outcomes.

Biom J 2019 Jan 23;61(1):8-26. Epub 2018 Oct 23.

Department of Statistics, The Ohio State University, Columbus, Ohio, USA.

Targeted therapies are becoming more common. In targeted therapy development, suppose its companion diagnostic test divides patients into a marker-positive subgroup and its complementary marker-negative subgroup. To find the right patient population for the therapy to target, inference on efficacy in the marker-positive and marker-negative subgroups as well as efficacy in the overall mixture population are all of interest. Read More

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http://doi.wiley.com/10.1002/bimj.201800002
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http://dx.doi.org/10.1002/bimj.201800002DOI Listing
January 2019
7 Reads

Design-based inference on Bernstein type estimators for continuous populations.

Biom J 2019 Jan 22;61(1):166-186. Epub 2018 Oct 22.

Department of Economics and Statistics, University of Siena, Piazza S. Francesco, 8, 53100, Siena, Italy.

The estimation of the values of a variable at any point of a study area is performed using Bernstein polynomials when the sampling scheme is implemented by selecting a point in each polygon of a regular grid overimposed onto the area. The evaluation of the precision of the resulting estimates is investigated under a completely design-based framework. Moreover, as the main contribution to the mean squared error of the Bernstein-type estimator is due to the bias, also a pseudo-jackknife estimator is proposed. Read More

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http://doi.wiley.com/10.1002/bimj.201800106
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http://dx.doi.org/10.1002/bimj.201800106DOI Listing
January 2019
10 Reads

Estimation and identification issues in the promotion time cure model when the same covariates influence long- and short-term survival.

Biom J 2018 Oct 22. Epub 2018 Oct 22.

Institut de statistique, biostatistique et sciences actuarielles (ISBA), Université catholique de Louvain, B-1348, Louvain-la-Neuve, Belgium.

The promotion time cure model is a survival model acknowledging that an unidentified proportion of subjects will never experience the event of interest whatever the duration of the follow-up. We focus our interest on the challenges raised by the strong posterior correlation between some of the regression parameters when the same covariates influence long- and short-term survival. Then, the regression parameters of shared covariates are strongly correlated with, in addition, identification issues when the maximum follow-up duration is insufficiently long to identify the cured fraction. Read More

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http://doi.wiley.com/10.1002/bimj.201700250
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http://dx.doi.org/10.1002/bimj.201700250DOI Listing
October 2018
9 Reads

Power priors based on multiple historical studies for binary outcomes.

Biom J 2018 Oct 16. Epub 2018 Oct 16.

Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.

Incorporating historical information into the design and analysis of a new clinical trial has been the subject of much recent discussion. For example, in the context of clinical trials of antibiotics for drug resistant infections, where patients with specific infections can be difficult to recruit, there is often only limited and heterogeneous information available from the historical trials. To make the best use of the combined information at hand, we consider an approach based on the multiple power prior that allows the prior weight of each historical study to be chosen adaptively by empirical Bayes. Read More

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http://doi.wiley.com/10.1002/bimj.201700246
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http://dx.doi.org/10.1002/bimj.201700246DOI Listing
October 2018
8 Reads

Covariate selection strategies for causal inference: Classification and comparison.

Biom J 2018 Oct 10. Epub 2018 Oct 10.

Department Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.

When causal effects are to be estimated from observational data, we have to adjust for confounding. A central aim of covariate selection for causal inference is therefore to determine a set that is sufficient for confounding adjustment, but other aims such as efficiency or robustness can be important as well. In this paper, we review six general approaches to covariate selection that differ in the targeted type of adjustment set. Read More

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http://doi.wiley.com/10.1002/bimj.201700294
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http://dx.doi.org/10.1002/bimj.201700294DOI Listing
October 2018
2 Reads

Multistate modeling and simulation of patient trajectories after allogeneic hematopoietic stem cell transplantation to inform drug development.

Biom J 2018 Sep 17. Epub 2018 Sep 17.

Biostatistical Sciences and Pharmacometrics, Novartis Pharma AG, Basel, Switzerland.

We present a case study for developing clinical trial scenarios in a complex progressive disease with multiple events of interest. The idea is to first capture the course of the disease in a multistate Markov model, and then to simulate clinical trials from this model, including a variety of hypothesized drug effects. This case study focuses on the prevention of graft-versus-host disease (GvHD) after allogeneic hematopoietic stem cell transplantation (HSCT). Read More

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http://doi.wiley.com/10.1002/bimj.201700285
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http://dx.doi.org/10.1002/bimj.201700285DOI Listing
September 2018
14 Reads

Neighbor balance and evenness of distribution of treatment replications in row-column designs.

Biom J 2018 Nov 5;60(6):1172-1189. Epub 2018 Oct 5.

Statistical Consulting Unit, Australian National University, Canberra, Australia.

Row-column designs allow error control in field experiments by blocking in two dimensions. While this strategy can capture spatial heterogeneity aligned with blocks and account for effects due to the farming operations along rows and columns, it suffers from the occasional clustered occurrence of several replications of the same treatment. This property of classical row-column designs has hampered their more widespread use in practice. Read More

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http://dx.doi.org/10.1002/bimj.201800013DOI Listing
November 2018
1 Read