1,337 results match your criteria Biometrical Journal [Journal]


Doug Altman: Driving critical appraisal and improvements in the quality of methodological and medical research.

Biom J 2020 Jul 8. Epub 2020 Jul 8.

Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK.

Doug Altman was a visionary leader and one of the most influential medical statisticians of the last 40 years. Based on a presentation in the "Invited session in memory of Doug Altman" at the 40th Annual Conference of the International Society for Clinical Biostatistics (ISCB) in Leuven, Belgium and our long-standing collaborations with Doug, we discuss his contributions to regression modeling, reporting, prognosis research, as well as some more general issues while acknowledging that we cannot cover the whole spectrum of Doug's considerable methodological output. His statement "To maximize the benefit to society, you need to not just do research but do it well" should be a driver for all researchers. Read More

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http://dx.doi.org/10.1002/bimj.202000053DOI Listing

A generalized transition model for grouped longitudinal categorical data.

Biom J 2020 Jul 6. Epub 2020 Jul 6.

School of Mathematics, Statistics, and Applied Mathematics, NUI Galway, Galway, Ireland.

Transition models are an important framework that can be used to model longitudinal categorical data. They are particularly useful when the primary interest is in prediction. The available methods for this class of models are suitable for the cases in which responses are recorded individually over time. Read More

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http://dx.doi.org/10.1002/bimj.201900394DOI Listing

Clustering with missing and left-censored data: A simulation study comparing multiple-imputation-based procedures.

Biom J 2020 Jul 6. Epub 2020 Jul 6.

Université de Paris, Sorbonne Paris Cité, ECSTRRA Team, INSERM UMR1153, Paris, France.

Cluster analysis, commonly used to explore large biomedical datasets, can be challenging, notably due to missing data or left-censored data induced by the sensitivity limits of the biochemical measurement method. Usually, complete-case analysis, simple imputation, or stochastic simple imputation are applied before clustering. More recently, consensus methods following multiple imputation have been proposed. Read More

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http://dx.doi.org/10.1002/bimj.201900366DOI Listing
July 2020
0.945 Impact Factor

Personalized treatment plans with multivariate outcomes.

Biom J 2020 Jul 6. Epub 2020 Jul 6.

Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY, USA.

In this work, we propose a novel method for individualized treatment selection when the treatment response is multivariate. Our method covers any number of treatments and it can be applied for a broad set of models. The proposed method uses a Mahalanobis-type distance measure to establish an ordering of treatments based on treatment performance measures. Read More

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http://dx.doi.org/10.1002/bimj.201800072DOI Listing

Information fraction estimation based on the number of events within the standard treatment regimen.

Biom J 2020 Jul 6. Epub 2020 Jul 6.

Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA.

For a Phase III randomized trial that compares survival outcomes between an experimental treatment versus a standard therapy, interim monitoring analysis is used to potentially terminate the study early based on efficacy. To preserve the nominal Type I error rate, alpha spending methods and information fractions are used to compute appropriate rejection boundaries in studies with planned interim analyses. For a one-sided trial design applied to a scenario in which the experimental therapy is superior to the standard therapy, interim monitoring should provide the opportunity to stop the trial prior to full follow-up and conclude that the experimental therapy is superior. Read More

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http://dx.doi.org/10.1002/bimj.201900236DOI Listing

Causal mediation analysis in nested case-control studies using conditional logistic regression.

Biom J 2020 Jun 30. Epub 2020 Jun 30.

Department of Statistics, Radiation Effects Research Foundation, Hiroshima, Hiroshima Prefecture, Japan.

The paper proposes an approach to causal mediation analysis in nested case-control study designs, often incorporated with countermatching schemes using conditional likelihood, and we compare the method's performance to that of mediation analysis using the Cox model for the full cohort with a continuous or dichotomous mediator. Simulation studies are conducted to assess our proposed method and investigate the efficiency relative to the cohort. We illustrate the method using actual data from two studies of potential mediation of radiation risk conducted within the Adult Health Study cohort of atomic-bomb survivors. Read More

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http://dx.doi.org/10.1002/bimj.201900120DOI Listing

Design-based mapping of tree attributes by 3P sampling.

Biom J 2020 Jun 28. Epub 2020 Jun 28.

CREA Research Centre for Forestry and Wood, Viale Santa Margherita, Arezzo, Italy.

The estimation of individual values (marks) in a finite population of units (e.g., trees) scattered onto a survey region is considered under 3P sampling. Read More

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http://dx.doi.org/10.1002/bimj.201900377DOI Listing

Component network meta-analysis compared to a matching method in a disconnected network: A case study.

Biom J 2020 Jun 28. Epub 2020 Jun 28.

Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.

Network meta-analysis is a method to combine evidence from randomized controlled trials (RCTs) that compare a number of different interventions for a given clinical condition. Usually, this requires a connected network. A possible approach to link a disconnected network is to add evidence from nonrandomized comparisons, using propensity score or matching-adjusted indirect comparisons methods. Read More

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http://dx.doi.org/10.1002/bimj.201900339DOI Listing

A flexible hierarchical framework for improving inference in area-referenced environmental health studies.

Biom J 2020 Jun 22. Epub 2020 Jun 22.

MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, UK.

Study designs where data have been aggregated by geographical areas are popular in environmental epidemiology. These studies are commonly based on administrative databases and, providing a complete spatial coverage, are particularly appealing to make inference on the entire population. However, the resulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typically are not available from routinely collected data. Read More

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http://dx.doi.org/10.1002/bimj.201900241DOI Listing

Parametric mode regression for bounded responses.

Biom J 2020 Jun 22. Epub 2020 Jun 22.

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

We propose new parametric frameworks of regression analysis with the conditional mode of a bounded response as the focal point of interest. Covariate effects estimation and prediction based on the maximum likelihood method under two new classes of regression models are demonstrated. We also develop graphical and numerical diagnostic tools to detect various sources of model misspecification. Read More

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http://dx.doi.org/10.1002/bimj.202000039DOI Listing

Bayesian confidence intervals for the difference between variances of delta-lognormal distributions.

Biom J 2020 Jun 22. Epub 2020 Jun 22.

Department of Applied Statistics, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.

Unnatural rainfall fluctuation can result in such severe natural phenomena as drought and floods. This variability not only occurs in areas with unusual natural features such as land formations and drainage but can also be due to human intervention. Since rainfall data often contain zero values, evaluating rainfall change is an important undertaking, which can be estimated via the confidence intervals for the difference between delta-lognormal variances using the highest posterior density-based reference (HPD-ref) and probability-matching (HPD-pm) priors. Read More

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http://dx.doi.org/10.1002/bimj.201900079DOI Listing

Impact of adolescent obesity on middle-age health of women given data MAR.

Biom J 2020 Jun 15. Epub 2020 Jun 15.

Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.

We analyze adolescent BMI and middle-age systolic blood pressure (SBP) repeatedly measured on women enrolled in the Fels Longitudinal Study (FLS) between 1929 and 2010 to address three questions: Do adolescent-specific growth rates in body mass index (BMI) and menarche affect middle-age SBP? Do they moderate the aging effect on middle-age SBP? Have the effects changed over historical time? To address the questions, we propose analyzing a growth curve model (GCM) that controls for age, birth-year cohort, and historical time. However, several complications in the data make the GCM analysis nonstandard. First, the person-specific adolescent BMI and middle-age SBP trajectories are unobservable. Read More

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http://dx.doi.org/10.1002/bimj.201900094DOI Listing

Bayesian profiling for cost with zeros to decompose total cost into probability of cost and mean nonzero cost.

Biom J 2020 Jun 15. Epub 2020 Jun 15.

VA Center of Innovation for Veteran-Centered and Value Driven Care, VA Eastern Colorado Health Care System, Aurora, CO, USA.

Cost of health care can vary substantially across hospitals, centers, or providers. Data from electronic health records provide information for studying patterns of cost variation and identifying high or low cost centers. Cost data often include zero values when patients receive no care, and joint two-part models have been developed for clustered cost data with zeros. Read More

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http://dx.doi.org/10.1002/bimj.201900148DOI Listing
June 2020
0.945 Impact Factor

Compound optimal allocations for survival clinical trials.

Biom J 2020 Jun 15. Epub 2020 Jun 15.

Unit of Biostatistics and Clinical Trials, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy.

The aim of the present paper is to provide optimal allocations for comparative clinical trials with survival outcomes. The suggested targets are derived adopting a compound optimization strategy based on a subjective weighting of the relative importance of inferential demands and ethical concerns. The ensuing compound optimal targets are continuous functions of the treatment effects, so we provide the conditions under which they can be approached by standard response-adaptive randomization procedures, also guaranteeing the applicability of the classical asymptotic inference. Read More

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http://dx.doi.org/10.1002/bimj.201900232DOI Listing

Joint modeling of interval counts of recurrent events and death.

Biom J 2020 Jun 14. Epub 2020 Jun 14.

Laboratory of Statistical Demography, Max Planck Institute for Demographic Research, Rostock, Germany.

When a recurrent event process is ended by death, this may imply dependent censoring if the two processes are associated. Such dependent censoring would have to be modeled to obtain a valid inference. Moreover, the dependence between the recurrence process and the terminal event may be the primary topic of interest. Read More

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http://dx.doi.org/10.1002/bimj.201900367DOI Listing

Sample size calculation based on precision for pilot sequential multiple assignment randomized trial (SMART).

Biom J 2020 Jun 11. Epub 2020 Jun 11.

Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.

The sequential multiple assignment randomized trial (SMART) is a design used to develop dynamic treatment regimes (DTRs). Given that DTRs are generally less well researched, pilot SMART studies are often necessary. One challenge in pilot SMART is to determine the sample size such that it is small yet meaningfully informative for future full-fledged SMART. Read More

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http://dx.doi.org/10.1002/bimj.201900364DOI Listing

Using a dose-finding benchmark to quantify the loss incurred by dichotomization in Phase II dose-ranging studies.

Biom J 2020 Jun 11. Epub 2020 Jun 11.

Service de Biostatistique et d'Epidemiologie & CESP OncoStat, INSERM, Institut Gustave Roussy, UVSQ, Villejuif, France.

While there is recognition that more informative clinical endpoints can support better decision-making in clinical trials, it remains a common practice to categorize endpoints originally measured on a continuous scale. The primary motivation for this categorization (and most commonly dichotomization) is the simplicity of the analysis. There is, however, a long argument that this simplicity can come at a high cost. Read More

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http://dx.doi.org/10.1002/bimj.201900332DOI Listing

Measuring intrarater association between correlated ordinal ratings.

Biom J 2020 Jun 11. Epub 2020 Jun 11.

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

Variability between raters' ordinal scores is commonly observed in imaging tests, leading to uncertainty in the diagnostic process. In breast cancer screening, a radiologist visually interprets mammograms and MRIs, while skin diseases, Alzheimer's disease, and psychiatric conditions are graded based on clinical judgment. Consequently, studies are often conducted in clinical settings to investigate whether a new training tool can improve the interpretive performance of raters. Read More

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http://dx.doi.org/10.1002/bimj.201900177DOI Listing

Variable selection with P-splines in functional linear regression: Application in graft-versus-host disease.

Biom J 2020 Jun 10. Epub 2020 Jun 10.

Department of Statistics, Universidad Carlos III de Madrid, Madrid, Spain.

This paper focuses on the problems of estimation and variable selection in the functional linear regression model (FLM) with functional response and scalar covariates. To this end, two different types of regularization (L and L ) are considered in this paper. On the one hand, a sample approach for functional LASSO in terms of basis representation of the sample values of the response variable is proposed. Read More

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http://dx.doi.org/10.1002/bimj.201900189DOI Listing

A pseudo-likelihood method for estimating misclassification probabilities in competing-risks settings when true-event data are partially observed.

Biom J 2020 Jun 10. Epub 2020 Jun 10.

Family AIDS Care and Education Services (FACES), Research Care and Training Program (RCTP), Kenya Medical Research Institute (KEMRI), Kisumu, Kenya.

Outcome misclassification occurs frequently in binary-outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause-specific hazards, cumulative incidence functions, and so forth. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification. Read More

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http://dx.doi.org/10.1002/bimj.201900198DOI Listing

Comparison of random-effects meta-analysis models for the relative risk in the case of rare events: A simulation study.

Biom J 2020 Jun 8. Epub 2020 Jun 8.

Faculty of Psychology and Sports Sciences, University of Münster, Münster, Germany.

Pooling the relative risk (RR) across studies investigating rare events, for example, adverse events, via meta-analytical methods still presents a challenge to researchers. The main reason for this is the high probability of observing no events in treatment or control group or both, resulting in an undefined log RR (the basis of standard meta-analysis). Other technical challenges ensue, for example, the violation of normality assumptions, or bias due to exclusion of studies and application of continuity corrections, leading to poor performance of standard approaches. Read More

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http://dx.doi.org/10.1002/bimj.201900379DOI Listing

A note on the interpretation of tree-based regression models.

Biom J 2020 May 25. Epub 2020 May 25.

Department of Statistics, Computer Science, Applications, University of Florence, Florence, Italy.

Tree-based models are a popular tool for predicting a response given a set of explanatory variables when the regression function is characterized by a certain degree of complexity. Sometimes, they are also used to identify important variables and for variable selection. We show that if the generating model contains chains of direct and indirect effects, then the typical variable importance measures suggest selecting as important mainly the background variables, which have a strong indirect effect, disregarding the variables that directly influence the response. Read More

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http://dx.doi.org/10.1002/bimj.201900195DOI Listing

Testing against umbrella or tree orderings for binomial proportions with an adaptation of an insect resistance case.

Biom J 2020 May 25. Epub 2020 May 25.

Department of Statistics and O.R., Complutense University of Madrid, Madrid, Spain.

Alternative hypotheses for order restrictions, such as umbrella or inverse umbrella (a.k.a tree) orderings, have been studied extensively in the literature, although less so when the studied response for each individual is the presence or absence of the event of interest. Read More

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http://dx.doi.org/10.1002/bimj.201900272DOI Listing

Propensity score methods for time-dependent cluster confounding.

Biom J 2020 May 18. Epub 2020 May 18.

ICES, Toronto, Ontario, Canada.

In observational studies, subjects are often nested within clusters. In medical studies, patients are often treated by doctors and therefore patients are regarded as nested or clustered within doctors. A concern that arises with clustered data is that cluster-level characteristics (e. Read More

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http://dx.doi.org/10.1002/bimj.201900277DOI Listing

Bayesian interval mapping of count trait loci based on zero-inflated generalized Poisson regression model.

Biom J 2020 May 12. Epub 2020 May 12.

Department of Statistics, School of Mathematical Sciences, Heilongjiang University, Harbin, P. R. China.

Count phenotypes with excessive zeros are often observed in the biological world. Researchers have studied many statistical methods for mapping the quantitative trait loci (QTLs) of zero-inflated count phenotypes. However, most of the existing methods consist of finding the approximate positions of the QTLs on the chromosome by genome-wide scanning. Read More

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http://dx.doi.org/10.1002/bimj.201900274DOI Listing

A weighted FDR procedure under discrete and heterogeneous null distributions.

Biom J 2020 May 4. Epub 2020 May 4.

Department of Statistical Science and Fox School of Business, Temple University, Philadelphia, PA, USA.

Multiple testing (MT) with false discovery rate (FDR) control has been widely conducted in the "discrete paradigm" where p-values have discrete and heterogeneous null distributions. However, in this scenario existing FDR procedures often lose some power and may yield unreliable inference, and for this scenario there does not seem to be an FDR procedure that partitions hypotheses into groups, employs data-adaptive weights and is nonasymptotically conservative. We propose a weighted p-value-based FDR procedure, "weighted FDR (wFDR) procedure" for short, for MT in the discrete paradigm that efficiently adapts to both heterogeneity and discreteness of p-value distributions. Read More

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http://dx.doi.org/10.1002/bimj.201900216DOI Listing

The area between ROC curves, a non-parametric method to evaluate a biomarker for patient treatment selection.

Biom J 2020 Apr 28. Epub 2020 Apr 28.

Service de Biostatistique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France.

Treatment selection markers are generally sought for when the benefit of an innovative treatment in comparison with a reference treatment is considered, and this benefit is suspected to vary according to the characteristics of the patients. Classically, such quantitative markers are detected through testing a marker-by-treatment interaction in a parametric regression model. Most alternative methods rely on modeling the risk of event occurrence in each treatment arm or the benefit of the innovative treatment over the marker values, but with assumptions that may be difficult to verify. Read More

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http://dx.doi.org/10.1002/bimj.201900171DOI Listing

Statistical models for complex data in clinical and epidemiological research.

Biom J 2020 May;62(3):528-531

Clinical Trials Unit, Faculty of Medicine and Medical Center, University of Freiburg, Germany.

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http://dx.doi.org/10.1002/bimj.202000079DOI Listing

Editorial: Year 2019 Report.

Biom J 2020 Jul 20;62(4):895-897. Epub 2020 Apr 20.

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http://dx.doi.org/10.1002/bimj.202000074DOI Listing

A model with space-varying regression coefficients for clustering multivariate spatial count data.

Biom J 2020 Apr 20. Epub 2020 Apr 20.

Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy.

Multivariate spatial count data are often segmented by unobserved space-varying factors that vary across space. In this setting, regression models that assume space-constant covariate effects could be too restrictive. Motivated by the analysis of cause-specific mortality data, we propose to estimate space-varying effects by exploiting a multivariate hidden Markov field. Read More

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http://dx.doi.org/10.1002/bimj.201900229DOI Listing

A Bayesian decision-theoretic approach to incorporate preclinical information into phase I oncology trials.

Biom J 2020 Apr 13. Epub 2020 Apr 13.

Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland.

Leveraging preclinical animal data for a phase I oncology trial is appealing yet challenging. In this paper, we use animal data to improve decision-making in a model-based dose-escalation procedure. We make a proposal for how to measure and address a prior-data conflict in a sequential study with a small sample size. Read More

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http://dx.doi.org/10.1002/bimj.201900161DOI Listing

Spatial auto-correlation and auto-regressive models estimation from sample survey data.

Biom J 2020 Apr 14. Epub 2020 Apr 14.

Istat, Directorate for Methodology and Statistical Process Design, Rome, Italy.

Maximum likelihood estimation of the model parameters for a spatial population based on data collected from a survey sample is usually straightforward when sampling and non-response are both non-informative, since the model can then usually be fitted using the available sample data, and no allowance is necessary for the fact that only a part of the population has been observed. Although for many regression models this naive strategy yields consistent estimates, this is not the case for some models, such as spatial auto-regressive models. In this paper, we show that for a broad class of such models, a maximum marginal likelihood approach that uses both sample and population data leads to more efficient estimates since it uses spatial information from sampled as well as non-sampled units. Read More

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http://dx.doi.org/10.1002/bimj.201800225DOI Listing

Allometric analysis using the multivariate shifted exponential normal distribution.

Biom J 2020 Apr 2. Epub 2020 Apr 2.

Dipartimento di Scienze Economiche e Sociali, Università Cattolica del Sacro Cuore, Piacenza, Emilia-Romagna, Italy.

In allometric studies, the joint distribution of the log-transformed morphometric variables is typically elliptical and with heavy tails. To account for these peculiarities, we introduce the multivariate shifted exponential normal (MSEN) distribution , an elliptical heavy-tailed generalization of the multivariate normal (MN). The MSEN belongs to the family of MN scale mixtures (MNSMs) by choosing a convenient shifted exponential as mixing distribution. Read More

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http://dx.doi.org/10.1002/bimj.201900248DOI Listing

Nonparametric confidence regions for the symmetry point-based optimal cutpoint and associated sensitivity of a continuous-scale diagnostic test.

Biom J 2020 Mar 30. Epub 2020 Mar 30.

Department of Statistical Sciences, University of Padua, Padua, Italy.

In medical research, diagnostic tests with continuous values are widely employed to attempt to distinguish between diseased and non-diseased subjects. The diagnostic accuracy of a test (or a biomarker) can be assessed by using the receiver operating characteristic (ROC) curve of the test. To summarize the ROC curve and primarily to determine an "optimal" threshold for test results to use in practice, several approaches may be considered, such as those based on the Youden index, on the so-called close-to-(0,1) point, on the concordance probability and on the symmetry point. Read More

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http://dx.doi.org/10.1002/bimj.201900222DOI Listing

Comparison of complex modeling strategies for prediction of a binary outcome based on a few, highly correlated predictors.

Biom J 2020 May 30;62(3):568-582. Epub 2020 Mar 30.

Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.

Motivated by a clinical prediction problem, a simulation study was performed to compare different approaches for building risk prediction models. Robust prediction models for hospital survival in patients with acute heart failure were to be derived from three highly correlated blood parameters measured up to four times, with predictive ability having explicit priority over interpretability. Methods that relied only on the original predictors were compared with methods using an expanded predictor space including transformations and interactions. Read More

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http://dx.doi.org/10.1002/bimj.201800243DOI Listing

Improved confidence intervals for a difference of two cause-specific cumulative incidence functions estimated in the presence of competing risks and random censoring.

Authors:
Emil Scosyrev

Biom J 2020 Mar 29. Epub 2020 Mar 29.

Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA.

A cause-specific cumulative incidence function (CIF) is the probability of failure from a specific cause as a function of time. In randomized trials, a difference of cause-specific CIFs (treatment minus control) represents a treatment effect. Cause-specific CIF in each intervention arm can be estimated based on the usual non-parametric Aalen-Johansen estimator which generalizes the Kaplan-Meier estimator of CIF in the presence of competing risks. Read More

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http://dx.doi.org/10.1002/bimj.201900060DOI Listing

Automatic variable selection for exposure-driven propensity score matching with unmeasured confounders.

Biom J 2020 May 23;62(3):868-884. Epub 2020 Mar 23.

Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.

Multivariable model building for propensity score modeling approaches is challenging. A common propensity score approach is exposure-driven propensity score matching, where the best model selection strategy is still unclear. In particular, the situation may require variable selection, while it is still unclear if variables included in the propensity score should be associated with the exposure and the outcome, with either the exposure or the outcome, with at least the exposure or with at least the outcome. Read More

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http://dx.doi.org/10.1002/bimj.201800190DOI Listing

Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers.

Biom J 2020 Mar 20. Epub 2020 Mar 20.

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

In clinical research and practice, landmark models are commonly used to predict the risk of an adverse future event, using patients' longitudinal biomarker data as predictors. However, these data are often observable only at intermittent visits, making their measurement times irregularly spaced and unsynchronized across different subjects. This poses challenges to conducting dynamic prediction at any post-baseline time. Read More

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http://dx.doi.org/10.1002/bimj.201900112DOI Listing

On a class of non-linear transformation cure rate models.

Biom J 2020 Mar 16. Epub 2020 Mar 16.

Department of Sociology, Panteion University of Social and Political Sciences, Athens, Greece.

In this paper, we propose a generalization of the mixture (binary) cure rate model, motivated by the existence of a zero-modified (inflation or deflation) distribution, on the initial number of causes, under a competing cause scenario. This non-linear transformation cure rate model is in the same form of models studied in the past; however, following our approach, we are able to give a realistic interpretation to a specific class of proper transformation functions, for the cure rate modeling. The estimation of the parameters is then carried out using the maximum likelihood method along with a profile approach. Read More

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http://dx.doi.org/10.1002/bimj.201900005DOI Listing

Early detection of high disease activity in juvenile idiopathic arthritis by sequential monitoring of patients' health-related quality of life scores.

Biom J 2020 Mar 11. Epub 2020 Mar 11.

Department of Biostatistics, University of Florida, Gainesville, FL, USA.

Juvenile idiopathic arthritis (JIA) is a chronic disease. During its "high disease activity (HDA)" stage, JIA can cause severe pain, and thus could seriously affect patients' physical and psychological health. Early detection of the HDA stage of JIA can reduce the damage of the disease by treating it at an early stage and alleviating the painful experience of the patients. Read More

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http://dx.doi.org/10.1002/bimj.201900127DOI Listing

A Bayesian seamless phase I-II trial design with two stages for cancer clinical trials with drug combinations.

Biom J 2020 Mar 9. Epub 2020 Mar 9.

Biostatistics and Bioinformatics Research Center, Samuel Oschin Comprehensive Cancer Institute, Los Angeles, CA, USA.

The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology recommends a one unique dose combination as "optimal," which may result in a subsequent failed phase II clinical trial since other dose combinations may present higher treatment efficacy for the same level of toxicity. We are particularly interested in the setting where it is necessary to wait a few cycles of therapy to observe an efficacy outcome and the phase I and II population of patients are different with respect to treatment efficacy. Read More

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http://dx.doi.org/10.1002/bimj.201900095DOI Listing

Corrected estimator of sensitive population proportion using unknown repeated trials in the unrelated question randomized response model.

Biom J 2020 Mar 6. Epub 2020 Mar 6.

Department of Mathematics, Guru Nanak Dev University, Amritsar, Punjab, India.

In this paper, we have pointed out a major mistake in the research paper of Singh and Mathur [(2004). Unknown repeated trials in the unrelated question randomized response model, Biometrical Journal, 46:375-378]. We have corrected this mistake and proposed the corresponding corrected estimator of sensitive population proportion. Read More

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http://dx.doi.org/10.1002/bimj.201900334DOI Listing
March 2020
0.945 Impact Factor

Estimating the distribution of heterogeneous treatment effects from treatment responses and from a predictive biomarker in a parallel-group RCT: A structural model approach.

Biom J 2020 May 4;62(3):697-711. Epub 2020 Mar 4.

Faculty of Medicine, Institute for Medical Information Processing, Biometry, and Epidemiology, LMU Munich, Munich, Germany.

When the objective is to administer the best of two treatments to an individual, it is necessary to know his or her individual treatment effects (ITEs) and the correlation between the potential responses (PRs) and under treatments 1 and 0. Data that are generated in a parallel-group design RCT does not allow the ITE to be determined because only two samples from the marginal distributions of these PRs are observed and not the corresponding joint distribution. This is due to the "fundamental problem of causal inference. Read More

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http://dx.doi.org/10.1002/bimj.201800370DOI Listing

Sample size recalculation in multicenter randomized controlled clinical trials based on noncomparative data.

Biom J 2020 Mar 4. Epub 2020 Mar 4.

Department of Medical Statistics, University Medical Centre Göttingen, Göttingen, Germany.

Many late-phase clinical trials recruit subjects at multiple study sites. This introduces a hierarchical structure into the data that can result in a power-loss compared to a more homogeneous single-center trial. Building on a recently proposed approach to sample size determination, we suggest a sample size recalculation procedure for multicenter trials with continuous endpoints. Read More

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http://dx.doi.org/10.1002/bimj.201900138DOI Listing

On the relation between the cause-specific hazard and the subdistribution rate for competing risks data: The Fine-Gray model revisited.

Biom J 2020 May 4;62(3):790-807. Epub 2020 Mar 4.

Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.

The Fine-Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling is that the approach considers individuals still at risk for an event of cause 1 after they fell victim to the competing risk of cause 2. The subdistribution hazard and the extended risk sets, where subjects who failed of the competing risk remain in the risk set, are generally perceived as unnatural . Read More

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http://dx.doi.org/10.1002/bimj.201800274DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7216972PMC

A potpourri of biostatistical research: Special Issue for ISCB ASC 2018.

Biom J 2020 03;62(2):267-269

Clinical Epidemiology & Biostatistics Unit, Murdoch Children's Research Institute & University of Melbourne, Melbourne, Australia.

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http://dx.doi.org/10.1002/bimj.202000030DOI Listing

Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R.

Biom J 2020 Mar 2. Epub 2020 Mar 2.

Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.

Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Read More

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http://dx.doi.org/10.1002/bimj.201900020DOI Listing

A regularized estimation approach for case-cohort periodic follow-up studies with an application to HIV vaccine trials.

Biom J 2020 Feb 20. Epub 2020 Feb 20.

Department of Statistics, University of Missouri, Columbia, MO, USA.

This paper discusses regression analysis of the failure time data arising from case-cohort periodic follow-up studies, and one feature of such data, which makes their analysis much more difficult, is that they are usually interval-censored rather than right-censored. Although some methods have been developed for general failure time data, there does not seem to exist an established procedure for the situation considered here. To address the problem, we present a semiparametric regularized procedure and develop a simple algorithm for the implementation of the proposed method. Read More

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http://dx.doi.org/10.1002/bimj.201900180DOI Listing
February 2020

Multiply imputing missing values arising by design in transplant survival data.

Biom J 2020 Feb 20. Epub 2020 Feb 20.

Statistics and Clinical Studies, NHS Blood and Transplant, Bristol, UK.

In this article, we address a missing data problem that occurs in transplant survival studies. Recipients of organ transplants are followed up from transplantation and their survival times recorded, together with various explanatory variables. Due to differences in data collection procedures in different centers or over time, a particular explanatory variable (or set of variables) may only be recorded for certain recipients, which results in this variable being missing for a substantial number of records in the data. Read More

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http://dx.doi.org/10.1002/bimj.201800253DOI Listing
February 2020

Modeling different behaviors in disclosing risk perception.

Biom J 2020 Feb 20. Epub 2020 Feb 20.

Department of Economics, Statistics and Finance "Giovanni Anania,", University of Calabria, Cosenza, Italy.

In many fields, people are requested to express their level of awareness about some risk (mainly associated with health, environment, energy, etc.) by selecting a category in an ordered scale. We propose a model for such ordinal data by taking into account that the observed response does not necessarily reflect the respondent's true opinion since the final answer can be inaccurate or completely random. Read More

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http://dx.doi.org/10.1002/bimj.201900006DOI Listing
February 2020