5,322 results match your criteria Biometrics[Journal]


Nonparametric analysis of nonhomogeneous multi-state processes based on clustered observations.

Biometrics 2020 Jul 8. Epub 2020 Jul 8.

Department of Biostatistics, Indiana University, Indiana, U.S.A.

Frequently, clinical trials and observational studies involve complex event history data with multiple events. When the observations are independent, the analysis of such studies can be based on standard methods for multi-state models. However, the independence assumption is often violated, such as in multicenter studies, which makes the use of standard methods improper. Read More

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http://dx.doi.org/10.1111/biom.13327DOI Listing

A bayesian hierarchical model for characterizing the diffusion of new antipsychotic drugs.

Biometrics 2020 Jul 6. Epub 2020 Jul 6.

Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA, 02115, USA.

New prescription medications are a primary driver of spending growth in the United States. For patients with severe mental illnesses, second generation antipsychotic (SGA) medications feature prominently. However, many SGAs are costly, particularly before generic entry, and some may increase the risk of diabetes. Read More

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http://dx.doi.org/10.1111/biom.13324DOI Listing

Estimation of incubation period and generation time based on observed length-biased epidemic cohort with censoring for COVID-19 outbreak in China.

Biometrics 2020 Jul 6. Epub 2020 Jul 6.

Beijing International Center for Mathematical Research, Peking University, Beijing, 100871, China.

The incubation period and generation time are key characteristics in the analysis of infectious diseases. The commonly used contact-tracing based estimation of incubation distribution is highly influenced by the individuals' judgment on the possible date of exposure, and might lead to significant errors. On the other hand, interval censoring based methods are able to utilize a much larger set of traveling data but may encounter biased sampling problems. Read More

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http://dx.doi.org/10.1111/biom.13325DOI Listing

Semiparametric estimation of cross-covariance functions for multivariate random fields.

Biometrics 2020 Jul 6. Epub 2020 Jul 6.

Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.

The prevalence of spatially referenced multivariate data has impelled researchers to develop procedures for joint modeling of multiple spatial processes. This ordinarily involves modeling marginal and cross-process dependence for any arbitrary pair of locations using a multivariate spatial covariance function. However, building a flexible multivariate spatial covariance function that is nonnegative definite is challenging. Read More

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http://dx.doi.org/10.1111/biom.13323DOI Listing

Structural factor equation models for causal network construction via directed acyclic mixed graphs.

Biometrics 2020 Jul 6. Epub 2020 Jul 6.

Department of Biostatistics, University of Michigan, Ann Arbor, MI.

Directed acyclic mixed graphs (DAMG) provide a useful representation of network topology with both directed and undirected edges subject to the restriction of no directed cycles in the graph. This graphical framework may arise in many biomedical studies, for example when a directed acyclic graph (DAG) of interest is contaminated with undirected edges induced by some unobserved confounding factors (e.g. Read More

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http://dx.doi.org/10.1111/biom.13322DOI Listing

Parametric g-formula implementations for causal survival analyses.

Biometrics 2020 Jun 26. Epub 2020 Jun 26.

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

The g-formula can be used to estimate the survival curve under a sustained treatment strategy. Two available estimators of the g-formula are noniterative conditional expectation and iterative conditional expectation. We propose a version of the iterative conditional expectation estimator and describe its procedures for deterministic and random treatment strategies. Read More

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http://dx.doi.org/10.1111/biom.13321DOI Listing

A constrained single-index regression for estimating interactions between a treatment and covariates.

Biometrics 2020 Jun 23. Epub 2020 Jun 23.

Department of Biostatistics, Columbia University, New York, New York.

We consider a single-index regression model, uniquely constrained to estimate interactions between a set of pretreatment covariates and a treatment variable on their effects on a response variable, in the context of analyzing data from randomized clinical trials. We represent interaction effect terms of the model through a set of treatment-specific flexible link functions on a linear combination of the covariates (a single index), subject to the constraint that the expected value given the covariates equals 0, while leaving the main effects of the covariates unspecified. We show that the proposed semiparametric estimator is consistent for the interaction term of the model, and that the efficiency of the estimator can be improved with an augmentation procedure. Read More

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http://dx.doi.org/10.1111/biom.13320DOI Listing

Scalable Bayesian matrix normal graphical models for brain functional networks.

Biometrics 2020 Jun 22. Epub 2020 Jun 22.

Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia.

Recently, there has been an explosive growth in graphical modeling approaches for estimating brain functional networks. In a detailed study, we show that surprisingly, standard graphical modeling approaches for fMRI data may not yield accurate estimates of the brain network due to the inability to suitably account for temporal correlations. We propose a novel Bayesian matrix normal graphical model that jointly models the temporal covariance and the brain network under a separable structure for the covariance to obtain improved estimates. Read More

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http://dx.doi.org/10.1111/biom.13319DOI Listing

Developing and evaluating risk prediction models with panel current status data.

Biometrics 2020 Jun 19. Epub 2020 Jun 19.

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

Panel current status data arise frequently in biomedical studies when the occurrence of a particular clinical condition is only examined at several prescheduled visit times. Existing methods for analyzing current status data have largely focused on regression modeling based on commonly used survival models such as the proportional hazards model and the accelerated failure time model. However, these procedures have the limitations of being difficult to implement and performing sub-optimally in relatively small sample sizes. Read More

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http://dx.doi.org/10.1111/biom.13317DOI Listing

Semiparametric regression calibration for general hazard models in survival analysis with covariate measurement error; surprising performance under linear hazard.

Biometrics 2020 Jun 18. Epub 2020 Jun 18.

Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia.

Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. Regression calibration (RC) is a common approach to correct for bias in regression analysis with covariate measurement error. In survival analysis with covariate measurement error, it is well known that the RC estimator may be biased when the hazard is an exponential function of the covariates. Read More

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http://dx.doi.org/10.1111/biom.13318DOI Listing

Spatial regression and spillover effects in cluster randomized trials with count outcomes.

Biometrics 2020 Jun 18. Epub 2020 Jun 18.

MRC Tropical Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.

This paper describes methodology for analyzing data from cluster randomized trials with count outcomes, taking indirect effects as well spatial effects into account. Indirect effects are modeled using a novel application of a measure of depth within the intervention arm. Both direct and indirect effects can be estimated accurately even when the proposed model is misspecified. Read More

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http://dx.doi.org/10.1111/biom.13316DOI Listing

Two-group Poisson-Dirichlet mixtures for multiple testing.

Biometrics 2020 Jun 14. Epub 2020 Jun 14.

Department of Statistics, Rice University, Houston, Texas.

The simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed by Efron, identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literature, many approaches have suggested using mixtures of Dirichlet Processes in the two-group model framework. Read More

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http://dx.doi.org/10.1111/biom.13314DOI Listing

Optimality of testing procedures for survival data in the nonproportional hazards setting.

Biometrics 2020 Jun 14. Epub 2020 Jun 14.

Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts.

Most statistical tests for treatment effects used in randomized clinical trials with survival outcomes are based on the proportional hazards assumption, which often fails in practice. Data from early exploratory studies may provide evidence of nonproportional hazards, which can guide the choice of alternative tests in the design of practice-changing confirmatory trials. We developed a test to detect treatment effects in a late-stage trial, which accounts for the deviations from proportional hazards suggested by early-stage data. Read More

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http://dx.doi.org/10.1111/biom.13315DOI Listing

Batch Bayesian optimization design for optimizing a neurostimulator.

Biometrics 2020 Jun 12. Epub 2020 Jun 12.

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

Recently, spinal epidural neurostimulation is being considered for rehabilitation of persons suffering from partial spinal-cord injury. The neurostimulator must be programmed by a neurosurgeon, yet little work has been done to develop rigorous methods for optimally programming the device. We propose an adaptive design to efficiently optimize programming of the neurostimulator based on specified interim evaluations of patient reported preferences. Read More

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http://dx.doi.org/10.1111/biom.13313DOI Listing

Evaluation of longitudinal surrogate markers.

Biometrics 2020 Jun 7. Epub 2020 Jun 7.

RAND Corporation.

The use of surrogate markers to examine the effectiveness of a treatment has the potential to decrease study length and identify effective treatments more quickly. Most available methods to investigate the usefulness of a surrogate marker involve restrictive parametric assumptions and tend to focus on settings where the surrogate is measured at a single point in time. However, in many clinical settings, the potential surrogate marker is often measured repeatedly over time, and thus, the surrogate marker information is a trajectory of measurements. Read More

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http://dx.doi.org/10.1111/biom.13310DOI Listing

Horvitz-Thompson-like estimation with distance-based detection probabilities for circular plot sampling of forests.

Biometrics 2020 Jun 7. Epub 2020 Jun 7.

School of Computing, University of Eastern Finland, Joensuu, Finland.

In circular plot sampling, trees within a given distance from the sample plot location constitute a sample, which is used to infer characteristics of interest for the forest area. If the sample is collected using a technical device located at the sampling point, eg, a terrestrial laser scanner, all trees of the sample plot cannot be observed because they hide behind each other. We propose a Horvitz-Thompson-like estimator with distance-based detection probabilities derived from stochastic geometry for estimation of population totals such as stem density and basal area in such situation. Read More

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http://dx.doi.org/10.1111/biom.13312DOI Listing

Estimating the optimal timing of surgery from observational data.

Biometrics 2020 Jun 7. Epub 2020 Jun 7.

Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas.

Infants with hypoplastic left heart syndrome require an initial Norwood operation, followed some months later by a stage 2 palliation (S2P). The timing of S2P is critical for the operation's success and the infant's survival, but the optimal timing, if one exists, is unknown. We attempt to identify the optimal timing of S2P by analyzing data from the Single Ventricle Reconstruction Trial (SVRT), which randomized patients between two different types of Norwood procedure. Read More

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http://dx.doi.org/10.1111/biom.13311DOI Listing

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 (CAR) 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, District of Columbia.

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 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.

Department of Epidemiology, Biostatistics and Occupational Health, 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 multisource 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, Minnesota.

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. Multisource 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.

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.

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, Belgium.

We propose a Bayesian latent Ornstein-Uhlenbeck (OU) 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