5,304 results match your criteria Biometrics [Journal]


The impact of misclassification on covariate-adaptive randomized clinical trials.

Authors:
Tong Wang Wei Ma

Biometrics 2020 May 26. Epub 2020 May 26.

Institute of Statistics and Big Data, Renmin University of China, Beijing, China.

Covariate-adaptive randomization is widely used in clinical trials to balance treatment allocation over covariates. Over the past decade, significant progress has been made on the theoretical properties of covariate-adaptive design and associated inference. However, most results are established under the assumption that the covariates are correctly measured. Read More

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http://dx.doi.org/10.1111/biom.13308DOI Listing

Quantile regression for survival data with covariates subject to detection limits.

Biometrics 2020 May 26. Epub 2020 May 26.

Department of Statistics, George Washington University, Washington, D.C., 20052, U.S.A.

With advances in biomedical research, biomarkers are becoming increasingly important prognostic factors for predicting overall survival, while the measurement of biomarkers is often censored due to instruments' lower limits of detection. This leads to two types of censoring: random censoring in overall survival outcomes and fixed censoring in biomarker covariates, posing new challenges in statistical modeling and inference. Existing methods for analyzing such data focus primarily on linear regression ignoring censored responses or semiparametric accelerated failure time (AFT) models with covariates under detection limits (DL). Read More

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http://dx.doi.org/10.1111/biom.13309DOI Listing

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

Biometrics 2020 May 21. Epub 2020 May 21.

Lady Davis Institite for Medical Research, Department of Epidemiology, Biostatistics and Occupational Health, Gerald Bronfman Department of Oncology, Department of Human Genetics, McGill University, Montreal, QC, Canada.

Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. Read More

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http://dx.doi.org/10.1111/biom.13307DOI Listing

Sensitivity analysis for subsequent treatments in confirmatory oncology clinical trials: A two-stage stochastic dynamic treatment regime approach.

Biometrics 2020 May 18. Epub 2020 May 18.

Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan.

Subsequent treatments can result in a difficulty in interpretation of the overall survival results in confirmatory oncology clinical trials. To complement the intention-to-treat (ITT) analysis affected by subsequent treatment patterns unintentional in the trial protocol, several causal methods targeting the per-protocol effect have been proposed. When two or more types of subsequent treatments are allowed in the trial protocol, however, these methods cannot answer clinical questions such as how sensitive the ITT analysis result is to higher or lower proportions of each subsequent treatment allowed in the trial protocol than observed, and to what extent ITT analysis result is generalizable to subsequent treatment patterns other than observed one. Read More

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http://dx.doi.org/10.1111/biom.13296DOI Listing

Robust and efficient semi-supervised estimation of average treatment effects with application to electronic health records data.

Biometrics 2020 May 15. Epub 2020 May 15.

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

We consider the problem of estimating the average treatment effect (ATE) in a semi-supervised learning setting, where a very small proportion of the entire set of observations are labeled with the true outcome but features predictive of the outcome are available among all observations. This problem arises, for example, when estimating treatment effects in electronic health records (EHR) data because gold-standard outcomes are often not directly observable from the records but are observed for a limited number of patients through small-scale manual chart review. We develop an imputation-based approach for estimating the ATE that is robust to misspecification of the imputation model. Read More

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http://dx.doi.org/10.1111/biom.13298DOI Listing

Flexible link functions in a joint hierarchical Gaussian process model.

Biometrics 2020 May 15. Epub 2020 May 15.

Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.

Many longitudinal studies often require jointly modeling a biomarker and an event outcome, in order to provide more accurate inference and dynamic prediction of disease progression. Cystic fibrosis (CF) studies have illustrated the benefits of these models, primarily examining the joint evolution of lung-function decline and survival. We propose a novel joint model within the shared-parameter framework that accommodates nonlinear lung-function trajectories, in order to provide more accurate inference on lung-function decline over time and to examine the association between evolution of lung function and risk of a pulmonary exacerbation (PE) event recurrence. Read More

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http://dx.doi.org/10.1111/biom.13291DOI Listing

Iterated multi-source exchangeability models for individualized inference with an application to mobile sensor data.

Biometrics 2020 May 15. Epub 2020 May 15.

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

Researchers are increasingly interested in using sensor technology to collect accurate activity information and make individualized inference about treatments, exposures, and policies. How to optimally combine population data with data from an individual remains an open question. Multi-source exchangeability models (MEMs) are a Bayesian approach for increasing precision by combining potentially heterogeneous supplemental data sources into analysis of a primary source. Read More

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http://dx.doi.org/10.1111/biom.13294DOI Listing

Estimating the burden of the opioid epidemic for adults and adolescents in Ohio counties.

Biometrics 2020 May 15. Epub 2020 May 15.

Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, North Carolina, USA.

Quantifying the opioid epidemic at the local level is a challenging problem that has important consequences on resource allocation. Adults and adolescents may exhibit different spatial trends and require different interventions and resources so it is important to examine the problem for each age group. In Ohio, surveillance data are collected at the county-level for each age group on measurable outcomes of the opioid epidemic, overdose deaths and treatment admissions. Read More

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http://dx.doi.org/10.1111/biom.13295DOI Listing

Estimating and inferring the maximum degree of stimulus-locked time-varying brain connectivity networks.

Biometrics 2020 May 15. Epub 2020 May 15.

Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois, USA.

Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental settings. However, these experimental settings bear little resemblance to our real-life experience in day-to-day interactions with the surroundings. To address this issue, neuroscientists have been measuring brain activity under natural viewing experiments in which the subjects are given continuous stimuli, such as watching a movie or listening to a story. Read More

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http://dx.doi.org/10.1111/biom.13297DOI Listing

Latent Ornstein-Uhlenbeck models for Bayesian analysis of multivariate longitudinal categorical responses.

Biometrics 2020 May 11. Epub 2020 May 11.

Department of Oral Health Sciences, KU Leuven, Leuven, 3000, Belgium.

We propose a Bayesian latent Ornstein-Uhlenbeck model to analyze unbalanced longitudinal data of binary and ordinal variables, which are manifestations of fewer continuous latent variables. We focus on the evolution of such latent variables when they continuously change over time. Existing approaches are limited to data collected at regular time intervals. Read More

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http://dx.doi.org/10.1111/biom.13292DOI Listing

Rejoinder for discussion on "Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories".

Biometrics 2020 May 7. Epub 2020 May 7.

Department of Statistics, University of California Irvine, Irvine, California.

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http://dx.doi.org/10.1111/biom.13273DOI Listing

On using electronic health records to improve optimal treatment rules in randomized trials.

Biometrics 2020 May 4. Epub 2020 May 4.

Department of Biostatistics, Mailman School of Public Health, Columbia University, New York City, New York.

Individualized treatment rules (ITRs) tailor medical treatments according to patient-specific characteristics in order to optimize patient outcomes. Data from randomized controlled trials (RCTs) are used to infer valid ITRs using statistical and machine learning methods. However, RCTs are usually conducted under specific inclusion/exclusion criteria, thus limiting their generalizability to a broader patient population in real-world practice settings. Read More

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http://dx.doi.org/10.1111/biom.13288DOI Listing

Bayesian group selection in logistic regression with application to MRI data analysis.

Biometrics 2020 May 4. Epub 2020 May 4.

Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio.

We consider Bayesian logistic regression models with group-structured covariates. In high-dimensional settings, it is often assumed that only a small portion of groups are significant, and thus, consistent group selection is of significant importance. While consistent frequentist group selection methods have been proposed, theoretical properties of Bayesian group selection methods for logistic regression models have not been investigated yet. Read More

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http://dx.doi.org/10.1111/biom.13290DOI Listing

A semiparametric Bayesian approach to population finding with time-to-event and toxicity data in a randomized clinical trial.

Biometrics 2020 Apr 27. Epub 2020 Apr 27.

Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan.

A utility-based Bayesian population finding (BaPoFi) method was proposed by Morita and Müller to analyze data from a randomized clinical trial with the aim of identifying good predictive baseline covariates for optimizing the target population for a future study. The approach casts the population finding process as a formal decision problem together with a flexible probability model using a random forest to define a regression mean function. BaPoFi is constructed to handle a single continuous or binary outcome variable. Read More

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http://dx.doi.org/10.1111/biom.13289DOI Listing

A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease.

Biometrics 2020 Apr 27. Epub 2020 Apr 27.

École d'Actuariat, Université Laval, Québec, Canada.

As the global burden of mental illness is estimated to become a severe issue in the near future, it demands the development of more effective treatments. Most psychiatric diseases are moderately to highly heritable and believed to involve many genes. Development of new treatment options demands more knowledge on the molecular basis of psychiatric diseases. Read More

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http://dx.doi.org/10.1111/biom.13286DOI Listing

Generalized reliability based on distances.

Biometrics 2020 Apr 27. Epub 2020 Apr 27.

Department of Accounting, Operations, and Information Systems, University of Alberta School of Business, Edmonton, Canada.

The intraclass correlation coefficient (ICC) is a classical index of measurement reliability. With the advent of new and complex types of data for which the ICC is not defined, there is a need for new ways to assess reliability. To meet this need, we propose a new distance-based ICC (dbICC), defined in terms of arbitrary distances among observations. Read More

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http://dx.doi.org/10.1111/biom.13287DOI Listing

Weighted regression analysis to correct for informative monitoring times and confounders in longitudinal studies.

Biometrics 2020 Apr 25. Epub 2020 Apr 25.

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

We address estimation of the marginal effect of a time-varying binary treatment on a continuous longitudinal outcome in the context of observational studies using electronic health records, when the relationship of interest is confounded, mediated, and further distorted by an informative visit process. We allow the longitudinal outcome to be recorded only sporadically and assume that its monitoring timing is informed by patients' characteristics. We propose two novel estimators based on linear models for the mean outcome that incorporate an adjustment for confounding and informative monitoring process through generalized inverse probability of treatment weights and a proportional intensity model, respectively. Read More

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http://dx.doi.org/10.1111/biom.13285DOI Listing

Comment on "Wang et al. (2005), Robust estimating functions and bias correction for longitudinal data analysis".

Biometrics 2020 Apr 20. Epub 2020 Apr 20.

Department of Statistical Sciences, University of Padova, Italy.

This note provides a discussion on the manuscript by Wang et al. (2005) who aim to robustify inference for longitudinal data analysis by replacing the ordinary generalized estimating function with an influence-bounded, possibly biased, version. To adjust for the bias of the ensuing robust estimator, the authors provide its analytic approximation by means of asymptotic expansions, and estimate it by plugging-in a nonrobust estimate of the parameter of interest. Read More

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http://dx.doi.org/10.1111/biom.13263DOI Listing

Repeated measures random forests (RMRF): Identifying factors associated with nocturnal hypoglycemia.

Biometrics 2020 Apr 20. Epub 2020 Apr 20.

Department of Mathematics and Statistics, San Diego State University, San Diego, California.

Nocturnal hypoglycemia is a common phenomenon among patients with diabetes and can lead to a broad range of adverse events and complications. Identifying factors associated with hypoglycemia can improve glucose control and patient care. We propose a repeated measures random forest (RMRF) algorithm that can handle nonlinear relationships and interactions and the correlated responses from patients evaluated over several nights. Read More

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http://dx.doi.org/10.1111/biom.13284DOI Listing

Rejoinder to "Comment on 'Wang et al. (2005), Robust estimating functions and bias correction for longitudinal data analysis' by Nicola Lunardon and Giovanna Menardi".

Biometrics 2020 Apr 20. Epub 2020 Apr 20.

Business School, University of Queensland, St. Lucia, QLD, Australia.

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http://dx.doi.org/10.1111/biom.13262DOI Listing

Parameter estimation for discretely observed linear birth-and-death processes.

Biometrics 2020 Apr 18. Epub 2020 Apr 18.

Department of Mathematics and Statistics, Masaryk University, Brno, Czech Republic.

Birth-and-death processes are widely used to model the development of biological populations. Although they are relatively simple models, their parameters can be challenging to estimate, as the likelihood can become numerically unstable when data arise from the most common sampling schemes, such as annual population censuses. A further difficulty arises when the discrete observations are not equi-spaced, for example, when census data are unavailable for some years. Read More

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http://dx.doi.org/10.1111/biom.13282DOI Listing

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies.

Biometrics 2020 Apr 15. Epub 2020 Apr 15.

Division of Research, Kaiser Permanente Northern California, Oakland, California.

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR-based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Read More

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http://dx.doi.org/10.1111/biom.13271DOI Listing

Efficient screening of predictive biomarkers for individual treatment selection.

Biometrics 2020 Apr 15. Epub 2020 Apr 15.

Department of Data Science, The Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan.

The development of molecular diagnostic tools to achieve individualized medicine requires identifying predictive biomarkers associated with subgroups of individuals who might receive beneficial or harmful effects from different available treatments. However, due to the large number of candidate biomarkers in the large-scale genetic and molecular studies, and complex relationships among clinical outcome, biomarkers, and treatments, the ordinary statistical tests for the interactions between treatments and covariates have difficulties from their limited statistical powers. In this paper, we propose an efficient method for detecting predictive biomarkers. Read More

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http://dx.doi.org/10.1111/biom.13279DOI Listing

Bayesian inference of causal effects from observational data in Gaussian graphical models.

Biometrics 2020 Apr 15. Epub 2020 Apr 15.

Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy.

We assume that multivariate observational data are generated from a distribution whose conditional independencies are encoded in a Directed Acyclic Graph (DAG). For any given DAG, the causal effect of a variable onto another one can be evaluated through intervention calculus. A DAG is typically not identifiable from observational data alone. Read More

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http://dx.doi.org/10.1111/biom.13281DOI Listing

Analyzing wearable device data using marked point processes.

Biometrics 2020 Apr 13. Epub 2020 Apr 13.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

This paper introduces two sets of measures as exploratory tools to study physical activity patterns: active-to-sedentary/sedentary-to-active rate function (ASRF/SARF) and active/sedentary rate function (ARF/SRF). These two sets of measures are complementary to each other and can be effectively used together to understand physical activity patterns. The specific features are illustrated by an analysis of wearable device data from National Health and Nutrition Examination Survey (NHANES). Read More

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http://dx.doi.org/10.1111/biom.13269DOI Listing

Upper bound estimators of the population size based on ordinal models for capture-recapture experiments.

Biometrics 2020 Apr 13. Epub 2020 Apr 13.

Consiglio Superiore della Magistratura, Rome, Italy.

Capture-recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health, and biodiversity are just a few examples. The estimation of the number of unseen units has been a challenge for theoretical statisticians, and considerable progress has been made in providing lower bound estimators for the population size. Read More

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http://dx.doi.org/10.1111/biom.13265DOI Listing

Bayesian analysis of survival data with missing censoring indicators.

Biometrics 2020 Apr 13. Epub 2020 Apr 13.

Department of Statistics, Florida State University, Tallahassee, Florida.

In some large clinical studies, it may be impractical to perform the physical examination to every subject at his/her last monitoring time in order to diagnose the occurrence of the event of interest. This gives rise to survival data with missing censoring indicators where the probability of missing may depend on time of last monitoring and some covariates. We present a fully Bayesian semi-parametric method for such survival data to estimate regression parameters of the proportional hazards model of Cox. Read More

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http://dx.doi.org/10.1111/biom.13280DOI Listing

Horseshoe-based Bayesian nonparametric estimation of effective population size trajectories.

Biometrics 2020 Apr 11. Epub 2020 Apr 11.

Department of Statistics, University of California Irvine, Irvine, California.

Phylodynamics is an area of population genetics that uses genetic sequence data to estimate past population dynamics. Modern state-of-the-art Bayesian nonparametric methods for recovering population size trajectories of unknown form use either change-point models or Gaussian process priors. Change-point models suffer from computational issues when the number of change-points is unknown and needs to be estimated. Read More

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http://dx.doi.org/10.1111/biom.13276DOI Listing

A Bayes factor approach with informative prior for rare genetic variant analysis from next generation sequencing data.

Biometrics 2020 Apr 10. Epub 2020 Apr 10.

Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.

The discovery of rare genetic variants through next generation sequencing is a very challenging issue in the field of human genetics. We propose a novel region-based statistical approach based on a Bayes Factor (BF) to assess evidence of association between a set of rare variants (RVs) located on the same genomic region and a disease outcome in the context of case-control design. Marginal likelihoods are computed under the null and alternative hypotheses assuming a binomial distribution for the RV count in the region and a beta or mixture of Dirac and beta prior distribution for the probability of RV. Read More

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http://dx.doi.org/10.1111/biom.13278DOI Listing

A powerful procedure that controls the false discovery rate with directional information.

Biometrics 2020 Apr 10. Epub 2020 Apr 10.

Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.

In many multiple testing applications in genetics, the signs of the test statistics provide useful directional information, such as whether genes are potentially up- or down-regulated between two experimental conditions. However, most existing procedures that control the false discovery rate (FDR) are P-value based and ignore such directional information. We introduce a novel procedure, the signed-knockoff procedure, to utilize the directional information and control the FDR in finite samples. Read More

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http://dx.doi.org/10.1111/biom.13277DOI Listing

Zero-inflated Poisson factor model with application to microbiome read counts.

Biometrics 2020 Apr 10. Epub 2020 Apr 10.

Department of Biostatistics, Mailman School of Public Health, Columbia University, New York.

Dimension reduction of high-dimensional microbiome data facilitates subsequent analysis such as regression and clustering. Most existing reduction methods cannot fully accommodate the special features of the data such as count-valued and excessive zero reads. We propose a zero-inflated Poisson factor analysis model in this paper. Read More

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http://dx.doi.org/10.1111/biom.13272DOI Listing

Transporting stochastic direct and indirect effects to new populations.

Biometrics 2020 Apr 11. Epub 2020 Apr 11.

Division of Biostatistics, University of California, Berkeley, California.

Transported mediation effects may contribute to understanding how interventions work differently when applied to new populations. However, we are not aware of any estimators for such effects. Thus, we propose two doubly robust, efficient estimators of transported stochastic (also called randomized interventional) direct and indirect effects. Read More

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http://dx.doi.org/10.1111/biom.13274DOI Listing

Retrospective versus prospective score tests for genetic association with case-control data.

Biometrics 2020 Apr 10. Epub 2020 Apr 10.

National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland.

Since the seminal work of Prentice and Pyke, the prospective logistic likelihood has become the standard method of analysis for retrospectively collected case-control data, in particular for testing the association between a single genetic marker and a disease outcome in genetic case-control studies. In the study of multiple genetic markers with relatively small effects, especially those with rare variants, various aggregated approaches based on the same prospective likelihood have been developed to integrate subtle association evidence among all the markers considered. Many of the commonly used tests are derived from the prospective likelihood under a common-random-effect assumption, which assumes a common random effect for all subjects. Read More

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http://dx.doi.org/10.1111/biom.13270DOI Listing
April 2020
1.568 Impact Factor

On computation of semiparametric maximum likelihood estimators with shape constraints.

Biometrics 2020 Apr 9. Epub 2020 Apr 9.

Department of Statistics, Florida State University, Florida.

Large sample theory of semiparametric models based on maximum likelihood estimation (MLE) with shape constraint on the nonparametric component is well studied. Relatively less attention has been paid to the computational aspect of semiparametric MLE. The computation of semiparametric MLE based on existing approaches such as the expectation-maximization (EM) algorithm can be computationally prohibitive when the missing rate is high. Read More

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http://dx.doi.org/10.1111/biom.13266DOI Listing

Discussion on "Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test" by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow.

Biometrics 2020 Apr 6. Epub 2020 Apr 6.

Serra Húnter fellow, Department of Statistics and Operations Research, Polytechnic University of Catalonia-BarcelonaTech, Barcelona, 08028, Spain.

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http://dx.doi.org/10.1111/biom.13251DOI Listing

Rejoinder to "Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test".

Biometrics 2020 Apr 6. Epub 2020 Apr 6.

Division of Biostatistics, College of Public Health, Ohio State University, Columbus, Ohio.

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http://dx.doi.org/10.1111/biom.13250DOI Listing

Discussion on "Predictively Consistent Prior Effective Sample Sizes" by Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, and Anthony O'Hagan.

Biometrics 2020 Apr 6. Epub 2020 Apr 6.

Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Anschutz Medical Campus, Aurora, Colorado.

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http://dx.doi.org/10.1111/biom.13253DOI Listing

Discussion on "Predictively consistent prior effective sample sizes," by Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, and Anthony O'Hagan.

Biometrics 2020 Apr 6. Epub 2020 Apr 6.

Department of Statistics & Data Science, University of Texas, Austin, Texas.

Neuenschwander et al. address a seemingly easy but often complicated problem in applied Bayesian methodology. We discuss some issues that relate to the question of why one might care about the effective sample size ( ) in a Bayesian model and the motivation for reporting the . Read More

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http://dx.doi.org/10.1111/biom.13254DOI Listing

Discussion of "Assessing the goodness-of-fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test," by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow.

Biometrics 2020 Apr 6. Epub 2020 Apr 6.

Department of Healthcare Administration, Asia University, Taichung, Taiwan, Republic of China.

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http://dx.doi.org/10.1111/biom.13255DOI Listing

Discussion on "Predictively consistent prior effective sample sizes," by Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, and Anthony O'Hagan.

Biometrics 2020 Apr 6. Epub 2020 Apr 6.

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

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http://dx.doi.org/10.1111/biom.13246DOI Listing

Discussion on "Predictively consistent prior effective sample sizes," by Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, and Anthony O'Hagan.

Biometrics 2020 Apr 6. Epub 2020 Apr 6.

Département de mathématiques et de statistique, Université de Montréal, Montréal, (Québec), Canada.

We extend the approach of finding effective sample size for a typical phase II clinical trial having efficacy and toxicity as two components of the response vector. The case of binary efficacy and binary toxicity is illustrated under Dirichlet and multivariate T priors. Read More

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http://dx.doi.org/10.1111/biom.13247DOI Listing
April 2020
1.568 Impact Factor

Adaptive treatment and robust control.

Biometrics 2020 Apr 6. Epub 2020 Apr 6.

Department of Engineering, Lancaster University, Lancaster, UK.

A control theory perspective on determination of optimal dynamic treatment regimes is considered. The aim is to adapt statistical methodology that has been developed for medical or other biostatistical applications to incorporate powerful control techniques that have been designed for engineering or other technological problems. Data tend to be sparse and noisy in the biostatistical area and interest has tended to be in statistical inference for treatment effects. Read More

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http://dx.doi.org/10.1111/biom.13268DOI Listing

Case contamination in electronic health records based case-control studies.

Biometrics 2020 Apr 4. Epub 2020 Apr 4.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.

Clinically relevant information from electronic health records (EHRs) permits derivation of a rich collection of phenotypes. Unlike traditionally designed studies where scientific hypotheses are specified a priori before data collection, the true phenotype status of any given individual in EHR-based studies is not directly available. Structured and unstructured data elements need to be queried through preconstructed rules to identify case and control groups. Read More

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http://dx.doi.org/10.1111/biom.13264DOI Listing

Improving inference for nonlinear state-space models of animal population dynamics given biased sequential life stage data.

Biometrics 2020 Apr 3. Epub 2020 Apr 3.

U.S. Fish and Wildlife Service, Lodi Field Office, Lodi, California.

State-space models (SSMs) are a popular tool for modeling animal abundances. Inference difficulties for simple linear SSMs are well known, particularly in relation to simultaneous estimation of process and observation variances. Several remedies to overcome estimation problems have been studied for relatively simple SSMs, but whether these challenges and proposed remedies apply for nonlinear stage-structured SSMs, an important class of ecological models, is less well understood. Read More

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http://dx.doi.org/10.1111/biom.13267DOI Listing

Bayesian latent multi-state modeling for nonequidistant longitudinal electronic health records.

Biometrics 2020 Mar 11. Epub 2020 Mar 11.

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

Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations. However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and often sparse observations. This suggests that the observed trajectories should be modeled via a latent continuous-time process potentially as a function of time-varying covariates. Read More

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http://dx.doi.org/10.1111/biom.13261DOI Listing
March 2020
1.568 Impact Factor

Report of the editors-2019.

Authors:

Biometrics 2020 03;76(1):5-8

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http://dx.doi.org/10.1111/biom.13215DOI Listing