1,267 results match your criteria Biometrical journal. Biometrische Zeitschrift[Journal]


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 Apr 20. 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

Construction and assessment of prediction rules for binary outcome in the presence of missing predictor data using multiple imputation and cross-validation: Methodological approach and data-based evaluation.

Biom J 2020 May 13;62(3):724-741. Epub 2020 Feb 13.

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

We investigate calibration and assessment of predictive rules when missing values are present in the predictors. Our paper has two key objectives. The first is to investigate how the calibration of the prediction rule can be combined with use of multiple imputation to account for missing predictor observations. Read More

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http://dx.doi.org/10.1002/bimj.201800289DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217034PMC

On the estimation of average treatment effects with right-censored time to event outcome and competing risks.

Biom J 2020 May 11;62(3):751-763. Epub 2020 Feb 11.

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

We are interested in the estimation of average treatment effects based on right-censored data of an observational study. We focus on causal inference of differences between t-year absolute event risks in a situation with competing risks. We derive doubly robust estimation equations and implement estimators for the nuisance parameters based on working regression models for the outcome, censoring, and treatment distribution conditional on auxiliary baseline covariates. Read More

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http://dx.doi.org/10.1002/bimj.201800298DOI Listing

Bayesian regression with spatiotemporal varying coefficients.

Biom J 2020 Feb 12. Epub 2020 Feb 12.

Department of Statistics, ITAM, Mexico.

To study the impact of climate variables on morbidity of some diseases in Mexico, we propose a spatiotemporal varying coefficients regression model. For that we introduce a new spatiotemporal-dependent process prior, in a Bayesian context, with identically distributed normal marginal distributions and joint multivariate normal distribution. We study its properties and characterise the dependence induced. Read More

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http://dx.doi.org/10.1002/bimj.201900098DOI Listing
February 2020

Skew-normal random-effects model for meta-analysis of diagnostic test accuracy (DTA) studies.

Biom J 2020 Feb 5. Epub 2020 Feb 5.

Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.

Hierarchical models are recommended for meta-analyzing diagnostic test accuracy (DTA) studies. The bivariate random-effects model is currently widely used to synthesize a pair of test sensitivity and specificity using logit transformation across studies. This model assumes a bivariate normal distribution for the random-effects. Read More

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http://dx.doi.org/10.1002/bimj.201900184DOI Listing
February 2020
0.945 Impact Factor

Nonlinear and time-dependent effects of sparsely measured continuous time-varying covariates in time-to-event analysis.

Biom J 2020 03 5;62(2):492-515. Epub 2020 Feb 5.

Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.

Many flexible extensions of the Cox proportional hazards model incorporate time-dependent (TD) and/or nonlinear (NL) effects of time-invariant covariates. In contrast, little attention has been given to the assessment of such effects for continuous time-varying covariates (TVCs). We propose a flexible regression B-spline-based model for TD and NL effects of a TVC. Read More

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http://dx.doi.org/10.1002/bimj.201900042DOI Listing

IV estimation without distributional assumptions.

Biom J 2020 May 5;62(3):688-696. Epub 2020 Feb 5.

Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

It is widely known that Instrumental Variable (IV) estimation allows the researcher to estimate causal effects between an exposure and an outcome even in face of serious uncontrolled confounding. The key requirement for IV estimation is the existence of a variable, the instrument, which only affects the outcome through its effects on the exposure and that the instrument-outcome relationship is unconfounded. Countless papers have employed such techniques and carefully addressed the validity of the IV assumption just mentioned. Read More

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http://dx.doi.org/10.1002/bimj.201800277DOI Listing

Joint analysis of panel count and interval-censored data using distribution-free frailty analysis.

Biom J 2020 Feb 5. Epub 2020 Feb 5.

Department of Medicine, David Geffen School of Medicine, University of California al Los Angeles, Los Angeles, CA, USA.

We propose a joint analysis of recurrent and nonrecurrent event data subject to general types of interval censoring. The proposed analysis allows for general semiparametric models, including the Box-Cox transformation and inverse Box-Cox transformation models for the recurrent and nonrecurrent events, respectively. A frailty variable is used to account for the potential dependence between the recurrent and nonrecurrent event processes, while leaving the distribution of the frailty unspecified. Read More

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http://dx.doi.org/10.1002/bimj.201900134DOI Listing
February 2020

A Bayesian spatiotemporal statistical analysis of out-of-hospital cardiac arrests.

Biom J 2020 Feb 3. Epub 2020 Feb 3.

Fondazione Ticino Cuore, Breganzona, Switzerland.

We propose a Bayesian spatiotemporal statistical model for predicting out-of-hospital cardiac arrests (OHCAs). Risk maps for Ticino, adjusted for demographic covariates, are built for explaining and forecasting the spatial distribution of OHCAs and their temporal dynamics. The occurrence intensity of the OHCA event in each area of interest, and the cardiac risk-based clustering of municipalities are efficiently estimated, through a statistical model that decomposes OHCA intensity into overall intensity, demographic fixed effects, spatially structured and unstructured random effects, time polynomial dependence, and spatiotemporal random effect. Read More

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http://dx.doi.org/10.1002/bimj.201900166DOI Listing
February 2020

Estimation in the Cox survival regression model with covariate measurement error and a changepoint.

Biom J 2020 Jan 31. Epub 2020 Jan 31.

Departments of Epidemiology, Biostatistics, Nutrition and Global Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

The Cox regression model is a popular model for analyzing the relationship between a covariate vector and a survival endpoint. The standard Cox model assumes a constant covariate effect across the entire covariate domain. However, in many epidemiological and other applications, the covariate of main interest is subject to a threshold effect: a change in the slope at a certain point within the covariate domain. Read More

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http://dx.doi.org/10.1002/bimj.201800085DOI Listing
January 2020

Bayesian latent class models for capture-recapture in the presence of missing data.

Biom J 2020 Jan 29. Epub 2020 Jan 29.

MEMOTEF, Sapienza University of Rome, Rome, Italy.

We propose a method for estimating the size of a population in a multiple record system in the presence of missing data. The method is based on a latent class model where the parameters and the latent structure are estimated using a Gibbs sampler. The proposed approach is illustrated through the analysis of a data set already known in the literature, which consists of five registrations of neural tube defects. Read More

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http://dx.doi.org/10.1002/bimj.201900111DOI Listing
January 2020

A nonparametric method of estimation of the population size in capture-recapture experiments.

Biom J 2020 Jan 29. Epub 2020 Jan 29.

Departament de Matemàtiques, Universitat Autònoma de Barcelona, Barcelona, Spain.

A recent method for estimating a lower bound of the population size in capture-recapture samples is studied. Specifically, some asymptotic properties, such as strong consistency and asymptotic normality, are provided. The introduced estimator is based on the empirical probability generating function (pgf) of the observed data, and it is consistent for count distributions having a log-convex pgf ( -class). Read More

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http://dx.doi.org/10.1002/bimj.201900185DOI Listing
January 2020

Estimating treatment effects with partially observed covariates using outcome regression with missing indicators.

Biom J 2020 03 29;62(2):428-443. Epub 2020 Jan 29.

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

Missing data is a common issue in research using observational studies to investigate the effect of treatments on health outcomes. When missingness occurs only in the covariates, a simple approach is to use missing indicators to handle the partially observed covariates. The missing indicator approach has been criticized for giving biased results in outcome regression. Read More

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http://dx.doi.org/10.1002/bimj.201900041DOI Listing

False discovery rate control for multiple testing based on discrete p-values.

Authors:
Xiongzhi Chen

Biom J 2020 Jan 20. Epub 2020 Jan 20.

Department of Mathematics and Statistics, Washington State University, Pullman, WA, USA.

For multiple testing based on discrete p-values, we propose a false discovery rate (FDR) procedure "BH+" with proven conservativeness. BH+ is at least as powerful as the BH (i.e. Read More

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http://dx.doi.org/10.1002/bimj.201900163DOI Listing
January 2020

Generalized parametric cure models for relative survival.

Biom J 2020 Jan 20. Epub 2020 Jan 20.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Cure models are used in time-to-event analysis when not all individuals are expected to experience the event of interest, or when the survival of the considered individuals reaches the same level as the general population. These scenarios correspond to a plateau in the survival and relative survival function, respectively. The main parameters of interest in cure models are the proportion of individuals who are cured, termed the cure proportion, and the survival function of the uncured individuals. Read More

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http://dx.doi.org/10.1002/bimj.201900056DOI Listing
January 2020

Approaches for missing covariate data in logistic regression with MNAR sensitivity analyses.

Biom J 2020 Jan 20. Epub 2020 Jan 20.

College of Medicine, Medical University of South Carolina, Charleston, SC, USA.

Data with missing covariate values but fully observed binary outcomes are an important subset of the missing data challenge. Common approaches are complete case analysis (CCA) and multiple imputation (MI). While CCA relies on missing completely at random (MCAR), MI usually relies on a missing at random (MAR) assumption to produce unbiased results. Read More

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http://dx.doi.org/10.1002/bimj.201900117DOI Listing
January 2020

Population size estimation with interval censored counts and external information: Prevalence of multiple sclerosis in Rome.

Biom J 2020 Jan 20. Epub 2020 Jan 20.

Department of Economics and Finance, University of Rome "Tor Vergata,", Rome, Italy.

We discuss Bayesian log-linear models for incomplete contingency tables with both missing and interval censored cells, with the aim of obtaining reliable population size estimates. We also discuss use of external information on the censoring probability, which may substantially reduce uncertainty. We show in simulation that information on lower bounds and external information can each improve the mean squared error of population size estimates, even when the external information is not completely accurate. Read More

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http://dx.doi.org/10.1002/bimj.201900268DOI Listing
January 2020

Smoothed empirical likelihood inference for ROC curve in the presence of missing biomarker values.

Biom J 2020 Jan 20. Epub 2020 Jan 20.

Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming, P. R. China.

This paper considers statistical inference for the receiver operating characteristic (ROC) curve in the presence of missing biomarker values by utilizing estimating equations (EEs) together with smoothed empirical likelihood (SEL). Three approaches are developed to estimate ROC curve and construct its SEL-based confidence intervals based on the kernel-assisted EE imputation, multiple imputation, and hybrid imputation combining the inverse probability weighted imputation and multiple imputation. Under some regularity conditions, we show asymptotic properties of the proposed maximum SEL estimators for ROC curve. Read More

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http://dx.doi.org/10.1002/bimj.201900121DOI Listing
January 2020

A design criterion for symmetric model discrimination based on flexible nominal sets.

Biom J 2020 Jan 20. Epub 2020 Jan 20.

Department of Applied Statistics, Johannes Kepler University Linz, Linz, Austria.

Experimental design applications for discriminating between models have been hampered by the assumption to know beforehand which model is the true one, which is counter to the very aim of the experiment. Previous approaches to alleviate this requirement were either symmetrizations of asymmetric techniques, or Bayesian, minimax, and sequential approaches. Here we present a genuinely symmetric criterion based on a linearized distance between mean-value surfaces and the newly introduced tool of flexible nominal sets. Read More

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http://dx.doi.org/10.1002/bimj.201900074DOI Listing
January 2020
0.945 Impact Factor

A simple method for correcting for the Will Rogers phenomenon with biometrical applications.

Biom J 2020 Jan 20. Epub 2020 Jan 20.

School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK.

In its basic form, the Will Rogers phenomenon takes place when an increase in the average value of each of two sets is achieved by moving an element from one set to another. This leads to the conclusion that there has been an improvement, when in fact essentially nothing has changed. Extended versions of this phenomenon can occur in epidemiological studies, rendering their results unreliable. Read More

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http://dx.doi.org/10.1002/bimj.201900199DOI Listing
January 2020

Modeling tails for collinear data with outliers in the English Longitudinal Study of Ageing: Quantile profile regression.

Biom J 2020 Jan 20. Epub 2020 Jan 20.

Department of Mathematics, Brunel University London, Uxbridge, UK.

Research has shown that high blood glucose levels are important predictors of incident diabetes. However, they are also strongly associated with other cardiometabolic risk factors such as high blood pressure, adiposity, and cholesterol, which are also highly correlated with one another. The aim of this analysis was to ascertain how these highly correlated cardiometabolic risk factors might be associated with high levels of blood glucose in older adults aged 50 or older from wave 2 of the English Longitudinal Study of Ageing (ELSA). Read More

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http://dx.doi.org/10.1002/bimj.201900146DOI Listing
January 2020

Developing risk models for multicenter data using standard logistic regression produced suboptimal predictions: A simulation study.

Biom J 2020 Jan 20. Epub 2020 Jan 20.

Department of Development and Regeneration, KU Leuven, Leuven, Belgium.

Although multicenter data are common, many prediction model studies ignore this during model development. The objective of this study is to evaluate the predictive performance of regression methods for developing clinical risk prediction models using multicenter data, and provide guidelines for practice. We compared the predictive performance of standard logistic regression, generalized estimating equations, random intercept logistic regression, and fixed effects logistic regression. Read More

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http://dx.doi.org/10.1002/bimj.201900075DOI Listing
January 2020