1,148 results match your criteria Biostatistics[Journal]


Multiway generalized canonical correlation analysis.

Biostatistics 2020 May 25. Epub 2020 May 25.

Laboratoire des Signaux et Systèmes (L2S), CNRS-CentraleSupélec, Université Paris-Saclay, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette cedex, France and Institut du Cerveau, INSERM U1127, CNRS UMR 7225, Sorbonne Universitè, F-75013, Paris, France.

Regularized generalized canonical correlation analysis (RGCCA) is a general multiblock data analysis framework that encompasses several important multivariate analysis methods such as principal component analysis, partial least squares regression, and several versions of generalized canonical correlation analysis. In this article, we extend RGCCA to the case where at least one block has a tensor structure. This method is called multiway generalized canonical correlation analysis (MGCCA). Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa010DOI Listing

Modeling continuous glucose monitoring (CGM) data during sleep.

Biostatistics 2020 May 22. Epub 2020 May 22.

Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.

We introduce a multilevel functional Beta model to quantify the blood glucose levels measured by continuous glucose monitors for multiple days in study participants with type 2 diabetes mellitus. The model estimates the subject-specific marginal quantiles, quantifies the within- and between-subject variability, and produces interpretable parameters of blood glucose dynamics as a function of time from the actigraphy-estimated sleep onset. Results are validated via simulations and by studying the association between the estimated model parameters and hemoglobin A1c, the gold standard for assessing glucose control in diabetes. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa023DOI Listing

Estimating diversity in networked ecological communities.

Biostatistics 2020 May 20. Epub 2020 May 20.

Department of Biostatistics and Department of Statistics, University of Washington, Health Sciences Building, 1959 NE Pacific St, Seattle WA 98195, USA.

Comparing ecological communities across environmental gradients can be challenging, especially when the number of different taxonomic groups in the communities is large. In this setting, community-level summaries called diversity indices are widely used to detect changes in the community ecology. However, estimation of diversity indices has received relatively little attention from the statistical community. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa015DOI Listing

Causal inference for recurrent event data using pseudo-observations.

Biostatistics 2020 May 20. Epub 2020 May 20.

Department of Decision Sciences, HEC Montréal, Montréal, Québec, Canada.

Recurrent event data are commonly encountered in observational studies where each subject may experience a particular event repeatedly over time. In this article, we aim to compare cumulative rate functions (CRFs) of two groups when treatment assignment may depend on the unbalanced distribution of confounders. Several estimators based on pseudo-observations are proposed to adjust for the confounding effects, namely inverse probability of treatment weighting estimator, regression model-based estimators, and doubly robust estimators. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa020DOI Listing

Evaluating biomarkers for treatment selection from reproducibility studies.

Biostatistics 2020 May 18. Epub 2020 May 18.

Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA 30602, USA.

We consider evaluating new or more accurately measured predictive biomarkers for treatment selection based on a previous clinical trial involving standard biomarkers. Instead of rerunning the clinical trial with the new biomarkers, we propose a more efficient approach which requires only either conducting a reproducibility study in which the new biomarkers and standard biomarkers are both measured on a set of patient samples, or adopting replicated measures of the error-contaminated standard biomarkers in the original study. This approach is easier to conduct and much less expensive than studies that require new samples from patients randomized to the intervention. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa018DOI Listing

Depth importance in precision medicine (DIPM): a tree- and forest-based method for right-censored survival outcomes.

Biostatistics 2020 May 18. Epub 2020 May 18.

Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Many clinical trials have been conducted to compare right-censored survival outcomes between interventions. Such comparisons are typically made on the basis of the entire group receiving one intervention versus the others. In order to identify subgroups for which the preferential treatment may differ from the overall group, we propose the depth importance in precision medicine (DIPM) method for such data within the precision medicine framework. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa021DOI Listing

New approaches for testing non-inferiority for three-arm trials with Poisson distributed outcomes.

Biostatistics 2020 May 8. Epub 2020 May 8.

Division of Biostatistics, Center for Devices and Radiological Health, Office Surveillance and Biometrics, FDA, USA.

With the availability of limited resources, innovation for improved statistical method for the design and analysis of randomized controlled trials (RCTs) is of paramount importance for newer and better treatment discovery for any therapeutic area. Although clinical efficacy is almost always the primary evaluating criteria to measure any beneficial effect of a treatment, there are several important other factors (e.g. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa014DOI Listing

Evaluating biomarkers for treatment selection from reproducibility studies.

Biostatistics 2020 May 7. Epub 2020 May 7.

Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA 30602, USA.

We consider evaluating new or more accurately measured predictive biomarkers for treatment selection based on a previous clinical trial involving standard biomarkers. Instead of rerunning the clinical trial with the new biomarkers, we propose a more efficient approach which requires only either conducting a reproducibility study in which the new biomarkers and standard biomarkers are both measured on a set of patient samples, or adopting replicated measures of the error-contaminated standard biomarkers in the original study. This approach is easier to conduct and much less expensive than studies that require new samples from patients randomized to the intervention. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa019DOI Listing

Direct modeling of the crude probability of cancer death and the number of life years lost due to cancer without the need of cause of death: a pseudo-observation approach in the relative survival setting.

Biostatistics 2020 May 6. Epub 2020 May 6.

Cancer Survival Group, Faculty of Epidemiology and Population Health, Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.

In population-based cancer studies, net survival is a crucial measure for population comparison purposes. However, alternative measures, namely the crude probability of death (CPr) and the number of life years lost (LYL) due to death according to different causes, are useful as complementary measures for reflecting different dimensions in terms of prognosis, treatment choice, or development of a control strategy. When the cause of death (COD) information is available, both measures can be estimated in competing risks setting using either cause-specific or subdistribution hazard regression models or with the pseudo-observation approach through direct modeling. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa017DOI Listing

Matrix decomposition for modeling lesion development processes in multiple sclerosis.

Biostatistics 2020 Apr 22. Epub 2020 Apr 22.

Department of Biostatistics, Brown University, Providence, RI 02903, USA.

Our main goal is to study and quantify the evolution of multiple sclerosis lesions observed longitudinally over many years in multi-sequence structural magnetic resonance imaging (sMRI). To achieve that, we propose a class of functional models for capturing the temporal dynamics and spatial distribution of the voxel-specific intensity trajectories in all sMRI sequences. To accommodate the hierarchical data structure (observations nested within voxels, which are nested within lesions, which, in turn, are nested within study participants), we use structured functional principal component analysis. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa016DOI Listing

Two-part joint model for a longitudinal semicontinuous marker and a terminal event with application to metastatic colorectal cancer data.

Biostatistics 2020 Apr 13. Epub 2020 Apr 13.

Department of Biostatistics, Bordeaux Population Health Research Center, INSERM U1219, 146 Rue Léo Saignat, 33076 Bordeaux, France.

Joint models for a longitudinal biomarker and a terminal event have gained interests for evaluating cancer clinical trials because the tumor evolution reflects directly the state of the disease. A biomarker characterizing the tumor size evolution over time can be highly informative for assessing treatment options and could be taken into account in addition to the survival time. The biomarker often has a semicontinuous distribution, i. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa012DOI Listing

A hidden Markov modeling approach for identifying tumor subclones in next-generation sequencing studies.

Biostatistics 2020 Apr 13. Epub 2020 Apr 13.

Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Dr, Rockville MD 20850 USA.

Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous as a combination of dominant and minor subclones with distinct copy number profiles. Estimating the clonal proportion and identifying mainclone and subclone genotypes across the genome are important for understanding tumor progression. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa013DOI Listing

A Bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks.

Biostatistics 2020 Apr 4. Epub 2020 Apr 4.

Department of Statistics, University of Florida, Union Rd, Gainesville, FL 32603, USA.

We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, but not vice versa (i.e., semi-competing risks). Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa008DOI Listing

Assessing the accuracy of predictive models with interval-censored data.

Biostatistics 2020 Mar 13. Epub 2020 Mar 13.

Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

We develop methods for assessing the predictive accuracy of a given event time model when the validation sample is comprised of case $K$ interval-censored data. An imputation-based, an inverse probability weighted (IPW), and an augmented inverse probability weighted (AIPW) estimator are developed and evaluated for the mean prediction error and the area under the receiver operating characteristic curve when the goal is to predict event status at a landmark time. The weights used for the IPW and AIPW estimators are obtained by fitting a multistate model which jointly considers the event process, the recurrent assessment process, and loss to follow-up. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa011DOI Listing

Geographically dependent individual-level models for infectious diseases transmission.

Biostatistics 2020 Mar 2. Epub 2020 Mar 2.

Department of Statistical Sciences, University of Toronto, Canada.

Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa009DOI Listing

A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics.

Biostatistics 2020 Feb 29. Epub 2020 Feb 29.

Department of Biostatistics, Johns Hopkins Bloomberg SPH, 615 N Wolfe St, Baltimore, MD 21205, USA; Department of Oncology, Johns Hopkins University School of Medicine SPH, 733 N Broadway, Baltimore, MD 21205, USA and Department of Epidemiology, Johns Hopkins Bloomberg SPH, 615 N Wolfe St, Baltimore, MD 21205, USA.

Cancers are routinely classified into subtypes according to various features, including histopathological characteristics and molecular markers. Previous genome-wide association studies have reported heterogeneous associations between loci and cancer subtypes. However, it is not evident what is the optimal modeling strategy for handling correlated tumor features, missing data, and increased degrees-of-freedom in the underlying tests of associations. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz065DOI Listing
February 2020

Randomization-based confidence intervals for cluster randomized trials.

Biostatistics 2020 Feb 29. Epub 2020 Feb 29.

Department of Biostatistics, Harvard University, T. H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA and Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA.

In a cluster randomized trial (CRT), groups of people are randomly assigned to different interventions. Existing parametric and semiparametric methods for CRTs rely on distributional assumptions or a large number of clusters to maintain nominal confidence interval (CI) coverage. Randomization-based inference is an alternative approach that is distribution-free and does not require a large number of clusters to be valid. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa007DOI Listing
February 2020

Estimation and inference for the population attributable risk in the presence of misclassification.

Biostatistics 2020 Feb 29. Epub 2020 Feb 29.

Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA, Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA and Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA 02115.

Because it describes the proportion of disease cases that could be prevented if an exposure were entirely eliminated from a target population as a result of an intervention, estimation of the population attributable risk (PAR) has become an important goal of public health research. In epidemiologic studies, categorical covariates are often misclassified. We present methods for obtaining point and interval estimates of the PAR and the partial PAR (pPAR) in the presence of misclassification, filling an important existing gap in public health evaluation methods. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz067DOI Listing
February 2020

The identity of two meta-analytic likelihoods and the ignorability of double-zero studies.

Biostatistics 2020 Feb 17. Epub 2020 Feb 17.

Department of Mathematics and Statistics, Thammasat University, Pathum Thani 12120, Thailand.

In meta-analysis, the conventional two-stage approach computes an effect estimate for each study in the first stage and proceeds with the analysis of effect estimates in the second stage. For counts of events as outcome, the risk ratio is often the effect measure of choice. However, if the meta-analysis includes many studies with no events the conventional method breaks down. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa004DOI Listing
February 2020

Corrigendum to: Neuroconductor: an R platform for medical imaging analysis.

Biostatistics 2020 Feb 17. Epub 2020 Feb 17.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA.

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http://dx.doi.org/10.1093/biostatistics/kxaa006DOI Listing
February 2020

RoBoT: a robust Bayesian hypothesis testing method for basket trials.

Biostatistics 2020 Feb 15. Epub 2020 Feb 15.

Department of Public Health Sciences, The University of Chicago, 5841 S. Maryland Ave, MC2000, Chicago, IL 60637, USA.

A basket trial in oncology encompasses multiple "baskets" that simultaneously assess one treatment in multiple cancer types or subtypes. It is well-recognized that hierarchical modeling methods, which adaptively borrow strength across baskets, can improve over simple pooling and stratification. We propose a novel Bayesian method, RoBoT (Robust Bayesian Hypothesis Testing), for the data analysis and decision-making in phase II basket trials. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa005DOI Listing
February 2020

Generalized additive regression for group testing data.

Biostatistics 2020 Feb 15. Epub 2020 Feb 15.

Department of Statistics, University of Nebraska-Lincoln, 340 Hardin Hall North, Lincoln, NE 68583, USA.

In screening applications involving low-prevalence diseases, pooling specimens (e.g., urine, blood, swabs, etc. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa003DOI Listing
February 2020

Bias due to Berkson error: issues when using predicted values in place of observed covariates.

Biostatistics 2020 Feb 10. Epub 2020 Feb 10.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA.

Studies often want to test for the association between an unmeasured covariate and an outcome. In the absence of a measurement, the study may substitute values generated from a prediction model. Justification for such methods can be found by noting that, with standard assumptions, this is equivalent to fitting a regression model for an outcome variable when at least one covariate is measured with Berkson error. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa002DOI Listing
February 2020

Regularized Bayesian transfer learning for population-level etiological distributions.

Biostatistics 2020 Feb 10. Epub 2020 Feb 10.

Department of Biostatistics, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205, USA.

Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsy) of a deceased individual, which are then aggregated to generate national and regional estimates of cause-specific mortality fractions. These estimates may be inaccurate if CCVA is trained on non-local training data different from the local population of interest. This problem is a special case of transfer learning, i. Read More

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http://dx.doi.org/10.1093/biostatistics/kxaa001DOI Listing
February 2020

Estimating disease onset from change points of markers measured with error.

Biostatistics 2020 Jan 30. Epub 2020 Jan 30.

Department of Statistics, Texas A&M University, College Station, TX 77843, USA.

Huntington disease is an autosomal dominant, neurodegenerative disease without clearly identified biomarkers for when motor-onset occurs. Current standards to determine motor-onset rely on a clinician's subjective judgment that a patient's extrapyramidal signs are unequivocally associated with Huntington disease. This subjectivity can lead to error which could be overcome using an objective, data-driven metric that determines motor-onset. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz068DOI Listing
January 2020

Simultaneous monitoring for regression coefficients and baseline hazard profile in Cox modeling of time-to-event data.

Biostatistics 2020 Jan 27. Epub 2020 Jan 27.

Department of Statistics, University of Connecticut, 215 Glenbrook Rd. U-4120, Storrs, CT 06269-4120, USA.

The Cox model is the most popular tool for analyzing time-to-event data. The nonparametric baseline hazard function can be as important as the regression coefficients in practice, especially when prediction is needed. In the context of stochastic process control, we propose a simultaneous monitoring method that combines a multivariate control chart for the regression coefficients and a profile control chart for the cumulative baseline hazard function that allows for data blocks of possibly different censoring rates and sample sizes. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz064DOI Listing
January 2020

Dynamic borrowing in the presence of treatment effect heterogeneity.

Biostatistics 2020 Jan 24. Epub 2020 Jan 24.

Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. SE, Minneapolis, MN 55455, USA.

A number of statistical approaches have been proposed for incorporating supplemental information in randomized clinical trials. Existing methods often compare the marginal treatment effects to evaluate the degree of consistency between sources. Dissimilar marginal treatment effects would either lead to increased bias or down-weighting of the supplemental data. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz066DOI Listing
January 2020

Sparse estimation for case-control studies with multiple disease subtypes.

Biostatistics 2020 Jan 24. Epub 2020 Jan 24.

Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer, World Health Organization, 150, Cours Albert Thomas, 69372 Lyon Cedex 08, France.

The analysis of case-control studies with several disease subtypes is increasingly common, e.g. in cancer epidemiology. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz063DOI Listing
January 2020

Adaptive group-regularized logistic elastic net regression.

Biostatistics 2019 Dec 30. Epub 2019 Dec 30.

Department of Epidemiology & Biostatistics, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, PO Box 7057, 1007 MB Amsterdam, The Netherlands and MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK.

In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) $p$-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection but is not straightforward. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz062DOI Listing
December 2019

Sufficient dimension reduction for compositional data.

Biostatistics 2019 Dec 30. Epub 2019 Dec 30.

Biostatistics Branch, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, USA.

Recent efforts to characterize the human microbiome and its relation to chronic diseases have led to a surge in statistical development for compositional data. We develop likelihood-based sufficient dimension reduction methods (SDR) to find linear combinations that contain all the information in the compositional data on an outcome variable, i.e. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz060DOI Listing
December 2019

Trans-ethnic meta-analysis of rare variants in sequencing association studies.

Biostatistics 2019 Dec 28. Epub 2019 Dec 28.

Thomas Francis, Jr. School of Public Health II, 1420 Washington Heights, Ann Arbor, MI 48109, USA.

Trans-ethnic meta-analysis is a powerful tool for detecting novel loci in genetic association studies. However, in the presence of heterogeneity among different populations, existing gene-/region-based rare variants meta-analysis methods may be unsatisfactory because they do not consider genetic similarity or dissimilarity among different populations. In response, we propose a score test under the modified random effects model for gene-/region-based rare variants associations. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz061DOI Listing
December 2019

Mapping epileptic directional brain networks using intracranial EEG data.

Biostatistics 2019 Dec 27. Epub 2019 Dec 27.

Department of Statistics, University of Virginia 148 Amphitheater Way, Charlottesville, VA 22904-4135, USA

The human brain is a directional network system, in which brain regions are network nodes and the influence exerted by one region on another is a network edge. We refer to this directional information flow from one region to another as directional connectivity. Seizures arise from an epileptic directional network; abnormal neuronal activities start from a seizure onset zone and propagate via a network to otherwise healthy brain regions. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz056DOI Listing
December 2019

Measuring effects of medication adherence on time-varying health outcomes using Bayesian dynamic linear models.

Biostatistics 2019 Dec 26. Epub 2019 Dec 26.

Department of Statistics, Harvard University, One Oxford Street, Cambridge, MA 02138 USA.

One of the most significant barriers to medication treatment is patients' non-adherence to a prescribed medication regimen. The extent of the impact of poor adherence on resulting health measures is often unknown, and typical analyses ignore the time-varying nature of adherence. This article develops a modeling framework for longitudinally recorded health measures modeled as a function of time-varying medication adherence. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz059DOI Listing
December 2019

A local group differences test for subject-level multivariate density neuroimaging outcomes.

Biostatistics 2019 Dec 26. Epub 2019 Dec 26.

Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA.

A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes can be quantified using summary statistics of voxel intensities. However, the manifestation of a disease process in imaging data is often unknown, or appears as a complex and nonlinear relationship between the voxel intensities on various modalities. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz058DOI Listing
December 2019

Covariate Assisted Principal regression for covariance matrix outcomes.

Biostatistics 2019 Dec 18. Epub 2019 Dec 18.

Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, 1200 Pressler St, Houston, TX 77030, USA.

In this study, we consider the problem of regressing covariance matrices on associated covariates. Our goal is to use covariates to explain variation in covariance matrices across units. As such, we introduce Covariate Assisted Principal (CAP) regression, an optimization-based method for identifying components associated with the covariates using a generalized linear model approach. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz057DOI Listing
December 2019

A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data.

Biostatistics 2019 Dec 17. Epub 2019 Dec 17.

Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

Microbiome omics approaches can reveal intriguing relationships between the human microbiome and certain disease states. Along with identification of specific bacteria taxa associated with diseases, recent scientific advancements provide mounting evidence that metabolism, genetics, and environmental factors can all modulate these microbial effects. However, the current methods for integrating microbiome data and other covariates are severely lacking. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz050DOI Listing
December 2019

A Gaussian copula approach for dynamic prediction of survival with a longitudinal biomarker.

Biostatistics 2019 Dec 10. Epub 2019 Dec 10.

Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.

Dynamic prediction uses patient information collected during follow-up to produce individualized survival predictions at given time points beyond treatment or diagnosis. This allows clinicians to obtain updated predictions of a patient's prognosis that can be used in making personalized treatment decisions. Two commonly used approaches for dynamic prediction are landmarking and joint modeling. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz049DOI Listing
December 2019

Pair-based likelihood approximations for stochastic epidemic models.

Biostatistics 2019 Dec 6. Epub 2019 Dec 6.

School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK.

Fitting stochastic epidemic models to data is a non-standard problem because data on the infection processes defined in such models are rarely observed directly. This in turn means that the likelihood of the observed data is intractable in the sense that it is very computationally expensive to obtain. Although data-augmented Markov chain Monte Carlo (MCMC) methods provide a solution to this problem, employing a tractable augmented likelihood, such methods typically deteriorate in large populations due to poor mixing and increased computation time. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz053DOI Listing
December 2019

Estimation of marginal causal effects in the presence of confounding by cluster.

Authors:
Arvid Sjölander

Biostatistics 2019 Dec 5. Epub 2019 Dec 5.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 171 77 Stockholm, Sweden.

A popular way to control for unmeasured confounders is to utilize clusters (e.g. sets of siblings), in which a potentially large set of confounders are constant. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz054DOI Listing
December 2019

Dynamic landmark prediction for mixture data.

Biostatistics 2019 Nov 23. Epub 2019 Nov 23.

Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143, USA and RAND Corporation, 1776 Main Street, Santa Monica, CA 90401, USA.

In kin-cohort studies, clinicians want to provide their patients with the most current cumulative risk of death arising from a rare deleterious mutation. Estimating the cumulative risk is difficult when the genetic mutation status is unknown and only estimated probabilities of a patient having the mutation are available. We estimate the cumulative risk for this scenario using a novel nonparametric estimator that incorporates covariate information and dynamic landmark prediction. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz052DOI Listing
November 2019

Statistical methods for biomarker data pooled from multiple nested case-control studies.

Biostatistics 2019 Nov 21. Epub 2019 Nov 21.

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

Pooling biomarker data across multiple studies allows for examination of a wider exposure range than generally possible in individual studies, evaluation of population subgroups and disease subtypes with more statistical power, and more precise estimation of biomarker-disease associations. However, circulating biomarker measurements often require calibration to a single reference assay prior to pooling due to assay and laboratory variability across studies. We propose several methods for calibrating and combining biomarker data from nested case-control studies when reference assay data are obtained from a subset of controls in each contributing study. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz051DOI Listing
November 2019

Machine learning for causal inference in Biostatistics.

Biostatistics 2020 04;21(2):336-338

Department of Biostatistics, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands.

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http://dx.doi.org/10.1093/biostatistics/kxz045DOI Listing

Can we learn individual-level treatment policies from clinical data?

Authors:
Uri Shalit

Biostatistics 2020 04;21(2):359-362

Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Technion City, Haifa 3200003, Israel.

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http://dx.doi.org/10.1093/biostatistics/kxz043DOI Listing

Regulatory oversight, causal inference, and safe and effective health care machine learning.

Biostatistics 2020 04;21(2):363-367

University of Michigan Law School, 625 State Street, Ann Arbor, MI, USA.

In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz044DOI Listing

From development to deployment: dataset shift, causality, and shift-stable models in health AI.

Biostatistics 2020 04;21(2):345-352

Department of Computer Science; Department of Applied Math & Statistics, and Department of Health Policy & Management, Johns Hopkins University, 160 Malone Hall, 3400 N. Charles Street, Baltimore, MD, USA.

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http://dx.doi.org/10.1093/biostatistics/kxz041DOI Listing

Teaching yourself about structural racism will improve your machine learning.

Biostatistics 2020 04;21(2):339-344

Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 DeSoto Street, 5131 Public Health Building, Pittsburgh, PA 15261-3100, USA.

In this commentary, we put forth the following argument: Anyone conducting machine learning in a health-related domain should educate themselves about structural racism. We argue that structural racism is a critical body of knowledge needed for generalizability in almost all domains of health research. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz040DOI Listing

The illness-death model for family studies.

Biostatistics 2019 Nov 19. Epub 2019 Nov 19.

Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada.

Family studies involve the selection of affected individuals from a disease registry who provide right-truncated ages of disease onset. Coarsened disease histories are then obtained from consenting family members, either through examining medical records, retrospective reporting, or clinical examination. Methods for dealing with such biased sampling schemes are available for continuous, binary, and failure time responses, but methods for more complex life history processes are less developed. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz048DOI Listing
November 2019

Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased machine learning.

Authors:
Iván Díaz

Biostatistics 2020 04;21(2):353-358

Division of Biostatistics, Weill Cornell Medicine, 402 East 67th Street, New York, NY 10065, USA.

In recent decades, the fields of statistical and machine learning have seen a revolution in the development of data-adaptive regression methods that have optimal performance under flexible, sometimes minimal, assumptions on the true regression functions. These developments have impacted all areas of applied and theoretical statistics and have allowed data analysts to avoid the biases incurred under the pervasive practice of parametric model misspecification. In this commentary, I discuss issues around the use of data-adaptive regression in estimation of causal inference parameters. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz042DOI Listing

Independence conditions and the analysis of life history studies with intermittent observation.

Biostatistics 2019 Nov 11. Epub 2019 Nov 11.

Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Multistate models provide a powerful framework for the analysis of life history processes when the goal is to characterize transition intensities, transition probabilities, state occupancy probabilities, and covariate effects thereon. Data on such processes are often only available at random visit times occurring over a finite period. We formulate a joint multistate model for the life history process, the recurrent visit process, and a random loss to follow-up time at which the visit process terminates. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz047DOI Listing
November 2019

A functional mixed model for scalar on function regression with application to a functional MRI study.

Biostatistics 2019 Oct 21. Epub 2019 Oct 21.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205, USA.

Motivated by a functional magnetic resonance imaging (fMRI) study, we propose a new functional mixed model for scalar on function regression. The model extends the standard scalar on function regression for repeated outcomes by incorporating subject-specific random functional effects. Using functional principal component analysis, the new model can be reformulated as a mixed effects model and thus easily fit. Read More

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http://dx.doi.org/10.1093/biostatistics/kxz046DOI Listing
October 2019