1,193 results match your criteria Biometrical Journal [Journal]


A transformation-based approach to Gaussian mixture density estimation for bounded data.

Authors:
Luca Scrucca

Biom J 2019 Apr 14. Epub 2019 Apr 14.

Department of Economics, Università degli Studi di Perugia, Italy.

Finite mixture of Gaussian distributions provide a flexible semiparametric methodology for density estimation when the continuous variables under investigation have no boundaries. However, in practical applications, variables may be partially bounded (e.g. 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.201800174DOI Listing
April 2019
1 Read

A coefficient of determination (R ) for generalized linear mixed models.

Biom J 2019 Apr 8. Epub 2019 Apr 8.

Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany.

Extensions of linear models are very commonly used in the analysis of biological data. Whereas goodness of fit measures such as the coefficient of determination (R ) or the adjusted R are well established for linear models, it is not obvious how such measures should be defined for generalized linear and mixed models. There are by now several proposals but no consensus has yet emerged as to the best unified approach in these settings. Read More

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http://dx.doi.org/10.1002/bimj.201800270DOI Listing

Dynamic prediction: A challenge for biostatisticians, but greatly needed by patients, physicians and the public.

Biom J 2019 Mar 25. Epub 2019 Mar 25.

Department of Hematology, Oncology and Stem Cell Transplantation, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.

Prognosis is usually expressed in terms of the probability that a patient will or will not have experienced an event of interest t years after diagnosis of a disease. This quantity, however, is of little informative value for a patient who is still event-free after a number of years. Such a patient would be much more interested in the conditional probability of being event-free in the upcoming t years, given that he/she did not experience the event in the s years after diagnosis, called "conditional survival. Read More

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http://dx.doi.org/10.1002/bimj.201800248DOI Listing
March 2019
4 Reads

Cox regression model with randomly censored covariates.

Biom J 2019 Mar 25. Epub 2019 Mar 25.

Department of Pediatrics, University of Texas Southwestern Medical School, Dallas, TX, USA.

This paper deals with a Cox proportional hazards regression model, where some covariates of interest are randomly right-censored. While methods for censored outcomes have become ubiquitous in the literature, methods for censored covariates have thus far received little attention and, for the most part, dealt with the issue of limit-of-detection. For randomly censored covariates, an often-used method is the inefficient complete-case analysis (CCA) which consists in deleting censored observations in the data analysis. Read More

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http://dx.doi.org/10.1002/bimj.201800275DOI Listing

Correcting for measurement error in fractional polynomial models using Bayesian modelling and regression calibration, with an application to alcohol and mortality.

Biom J 2019 May 20;61(3):558-573. Epub 2019 Mar 20.

Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Exposure measurement error can result in a biased estimate of the association between an exposure and outcome. When the exposure-outcome relationship is linear on the appropriate scale (e.g. Read More

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http://dx.doi.org/10.1002/bimj.201700279DOI Listing

Multiple imputation for discrete data: Evaluation of the joint latent normal model.

Biom J 2019 Mar 14. Epub 2019 Mar 14.

Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.

Missing data are ubiquitous in clinical and social research, and multiple imputation (MI) is increasingly the methodology of choice for practitioners. Two principal strategies for imputation have been proposed in the literature: joint modelling multiple imputation (JM-MI) and full conditional specification multiple imputation (FCS-MI). While JM-MI is arguably a preferable approach, because it involves specification of an explicit imputation model, FCS-MI is pragmatically appealing, because of its flexibility in handling different types of variables. Read More

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http://doi.wiley.com/10.1002/bimj.201800222
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http://dx.doi.org/10.1002/bimj.201800222DOI Listing
March 2019
12 Reads

Defective regression models for cure rate modeling with interval-censored data.

Biom J 2019 Mar 14. Epub 2019 Mar 14.

Institute of Mathematical Science and Computing, University of São Paulo, São Carlos, SP, Brazil.

Regression models in survival analysis are most commonly applied for right-censored survival data. In some situations, the time to the event is not exactly observed, although it is known that the event occurred between two observed times. In practice, the moment of observation is frequently taken as the event occurrence time, and the interval-censored mechanism is ignored. Read More

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http://doi.wiley.com/10.1002/bimj.201800056
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http://dx.doi.org/10.1002/bimj.201800056DOI Listing
March 2019
4 Reads

Predictive functional ANOVA models for longitudinal analysis of mandibular shape changes.

Biom J 2019 Mar 13. Epub 2019 Mar 13.

Department of Economics, University G. d'Annunzio, Chieti-Pescara, Italy.

In this paper, we introduce a Bayesian statistical model for the analysis of functional data observed at several time points. Examples of such data include the Michigan growth study where we wish to characterize the shape changes of human mandible profiles. The form of the mandible is often used by clinicians as an aid in predicting the mandibular growth. Read More

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http://dx.doi.org/10.1002/bimj.201800228DOI Listing

Confidence intervals for the difference in the success rates of two treatments in the analysis of correlated binary responses.

Biom J 2019 Mar 6. Epub 2019 Mar 6.

Department of Statistics, Texas A&M University, College Station, TX, USA.

In clinical studies, we often compare the success rates of two treatment groups where post-treatment responses of subjects within clusters are usually correlated. To estimate the difference between the success rates, interval estimation procedures that do not account for this intraclass correlation are likely inappropriate. To address this issue, we propose three interval procedures by direct extensions of recently proposed methods for independent binary data based on the concepts of design effect and effective sample size used in sample surveys. Read More

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http://dx.doi.org/10.1002/bimj.201700089DOI Listing

Normal frailty probit model for clustered interval-censored failure time data.

Biom J 2019 Mar 6. Epub 2019 Mar 6.

Department of Statistics, University of South Carolina, Columbia, SC, USA.

Clustered interval-censored data commonly arise in many studies of biomedical research where the failure time of interest is subject to interval-censoring and subjects are correlated for being in the same cluster. A new semiparametric frailty probit regression model is proposed to study covariate effects on the failure time by accounting for the intracluster dependence. Under the proposed normal frailty probit model, the marginal distribution of the failure time is a semiparametric probit model, the regression parameters can be interpreted as both the conditional covariate effects given frailty and the marginal covariate effects up to a multiplicative constant, and the intracluster association can be summarized by two nonparametric measures in simple and explicit form. Read More

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http://doi.wiley.com/10.1002/bimj.201800114
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http://dx.doi.org/10.1002/bimj.201800114DOI Listing
March 2019
5 Reads

Advaced Topics in Biostatistics: Editorial for the ISCB38 Special Issue.

Biom J 2019 Mar;61(2):243-244

Department of Statistics and Operations Research, SiDOR Research Group & CINBIO, Universidade de Vigo, Vigo, Spain.

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http://dx.doi.org/10.1002/bimj.201900059DOI Listing

Effect size measures and their benchmark values for quantifying benefit or risk of medicinal products.

Biom J 2019 Feb 28. Epub 2019 Feb 28.

idv - Data Analysis and Study Planning, Gauting, Germany.

The standardized mean difference is a well-known effect size measure for continuous, normally distributed data. In this paper we present a general basis for important other distribution families. As a general concept, usable for every distribution family, we introduce the relative effect, also called Mann-Whitney effect size measure of stochastic superiority. Read More

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http://dx.doi.org/10.1002/bimj.201800107DOI Listing
February 2019

Editorial: Year 2018 report.

Biom J 2019 May 26;61(3):783-786. Epub 2019 Feb 26.

Department of Mathematical Sciences, University of Southampton, Highfield, Southampton, UK.

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http://dx.doi.org/10.1002/bimj.201900069DOI Listing

Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants.

Biom J 2019 Feb 20. Epub 2019 Feb 20.

Quantitative Health Sciences and The Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA.

The evolution of "informatics" technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high-dimensionality of omics-type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. Read More

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http://dx.doi.org/10.1002/bimj.201700323DOI Listing
February 2019

Time-dependent mediators in survival analysis: Modeling direct and indirect effects with the additive hazards model.

Biom J 2019 Feb 19. Epub 2019 Feb 19.

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

We discuss causal mediation analyses for survival data and propose a new approach based on the additive hazards model. The emphasis is on a dynamic point of view, that is, understanding how the direct and indirect effects develop over time. Hence, importantly, we allow for a time varying mediator. Read More

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http://doi.wiley.com/10.1002/bimj.201800263
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http://dx.doi.org/10.1002/bimj.201800263DOI Listing
February 2019
7 Reads

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 May 29;61(3):665-687. 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

Powerful testing via hierarchical linkage disequilibrium in haplotype association studies.

Biom J 2019 May 28;61(3):747-768. 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
May 2019
18 Reads

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

Biom J 2019 Mar 25;61(2):264-274. 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
March 2019
5 Reads

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

Biom J 2019 May 16;61(3):769-778. 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
May 2019
1 Read

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

Biom J 2019 May 15;61(3):714-728. 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
May 2019
1 Read

Editorial for the MCP 2017 Special Issue.

Biom J 2019 01;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 01;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 Mar 9;61(2):379-390. 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
March 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 May 7;61(3):698-713. 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
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461520PMC
May 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 May 4;61(3):688-697. 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
May 2019
7 Reads

The hierarchical metaregression approach and learning from clinical evidence.

Biom J 2019 May 2;61(3):535-557. 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
May 2019
10 Reads

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

Biom J 2019 Mar 27;61(2):424-441. 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
March 2019
10 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 2019 Mar 13;61(2):299-318. 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
March 2019
2 Reads

Testing strategy in phase 3 trials with multiple doses.

Biom J 2019 01 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 2019 May 12;61(3):652-664. 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
May 2019
1 Read

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

Biom J 2019 May 10;61(3):729-746. 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
May 2019
3 Reads

Estimating marginal proportions and intraclass correlations with clustered binary data.

Biom J 2019 May 11;61(3):574-599. 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
May 2019
3 Reads

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

Biom J 2019 May 10;61(3):630-651. 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
May 2019
1 Read

Multiset sparse redundancy analysis for high-dimensional omics data.

Biom J 2019 Mar 3;61(2):406-423. 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
March 2019
8 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 2019 May 5;61(3):616-629. 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
May 2019
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A beta-binomial mixed-effects model approach for analysing longitudinal discrete and bounded outcomes.

Biom J 2019 May 27;61(3):600-615. 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
May 2019
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A Bayesian joint model of recurrent events and a terminal event.

Biom J 2019 01 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
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6450683PMC
January 2019
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Penalized likelihood and multiple testing.

Biom J 2019 01 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
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Semiparametric transformation models for interval-censored data in the presence of a cure fraction.

Biom J 2019 01 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
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Adjusting for selection bias in assessing treatment effect estimates from multiple subgroups.

Authors:
Ekkehard Glimm

Biom J 2019 01 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
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Confidence regions for treatment effects in subgroups in biomarker stratified designs.

Biom J 2019 01 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
14 Reads