4,999 results match your criteria Biometrics [Journal]


Threshold regression to accommodate a censored covariate.

Biometrics 2018 Jun 22. Epub 2018 Jun 22.

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

In several common study designs, regression modeling is complicated by the presence of censored covariates. Examples of such covariates include maternal age of onset of dementia that may be right censored in an Alzheimer's amyloid imaging study of healthy subjects, metabolite measurements that are subject to limit of detection censoring in a case-control study of cardiovascular disease, and progressive biomarkers whose baseline values are of interest, but are measured post-baseline in longitudinal neuropsychological studies of Alzheimer's disease. We propose threshold regression approaches for linear regression models with a covariate that is subject to random censoring. Read More

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June 2018
2 Reads

Physical activity classification with dynamic discriminative methods.

Biometrics 2018 Jun 19. Epub 2018 Jun 19.

Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts, U.S.A.

A person's physical activity has important health implications, so it is important to be able to measure aspects of physical activity objectively. One approach to doing that is to use data from an accelerometer to classify physical activity according to activity type (e.g. Read More

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Sample size determination for GEE analyses of stepped wedge cluster randomized trials.

Biometrics 2018 Jun 19. Epub 2018 Jun 19.

Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.

In stepped wedge cluster randomized trials, intact clusters of individuals switch from control to intervention from a randomly assigned period onwards. Such trials are becoming increasingly popular in health services research. When a closed cohort is recruited from each cluster for longitudinal follow-up, proper sample size calculation should account for three distinct types of intraclass correlations: the within-period, the inter-period, and the within-individual correlations. Read More

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Identifying disease-associated copy number variations by a doubly penalized regression model.

Biometrics 2018 Jun 12. Epub 2018 Jun 12.

Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.

Copy number variation (CNV) of DNA plays an important role in the development of many diseases. However, due to the irregularity and sparsity of the CNVs, studying the association between CNVs and a disease outcome or a trait can be challenging. Up to now, not many methods have been proposed in the literature for this problem. Read More

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June 2018
1 Read

Convex mixture regression for quantitative risk assessment.

Biometrics 2018 Jun 12. Epub 2018 Jun 12.

Department of Statistical Science, Duke University, Durham, North Carolina, U.S.A.

There is wide interest in studying how the distribution of a continuous response changes with a predictor. We are motivated by environmental applications in which the predictor is the dose of an exposure and the response is a health outcome. A main focus in these studies is inference on dose levels associated with a given increase in risk relative to a baseline. Read More

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An asymptotic approximation to the N-mixture model for the estimation of disease prevalence.

Biometrics 2018 Jun 5. Epub 2018 Jun 5.

Department of Statistics, Oregon State University, Corvallis, Oregon, U.S.A.

N-mixture models are probability models that estimate abundance using replicate observed counts while accounting for imperfect detection. In this article, we propose an asymptotic approximation to the N-mixture model which efficiently estimates large abundances without the computational limitations of the generalized N-mixture model introduced by Dail and Madsen in . It has been suggested in the literature that N-mixture models do not perform well when counts from the same sites show weak patterns of population dynamics. Read More

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June 2018
2 Reads

Bayesian optimal interval design with multiple toxicity constraints.

Authors:
Ruitao Lin

Biometrics 2018 Jun 5. Epub 2018 Jun 5.

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A.

Most phase I dose-finding trials are conducted based on a single binary toxicity outcome to investigate the safety of new drugs. In many situations, however, it is important to distinguish between various toxicity types and different toxicity grades. By minimizing the maximum joint probability of incorrect decisions, we extend the Bayesian optimal interval (BOIN) design to control multiple toxicity outcomes at prespecified levels. Read More

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Semiparametric regression analysis of interval-censored data with informative dropout.

Biometrics 2018 Jun 5. Epub 2018 Jun 5.

Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, U.S.A.

Interval-censored data arise when the event time of interest can only be ascertained through periodic examinations. In medical studies, subjects may not complete the examination schedule for reasons related to the event of interest. In this article, we develop a semiparametric approach to adjust for such informative dropout in regression analysis of interval-censored data. Read More

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Rejoinder: Time-dynamic profiling with application to hospital readmission among patients on dialysis.

Biometrics 2018 Jun 5. Epub 2018 Jun 5.

Department of Biostatistics, University of California, Los Angeles, Connecticut 90095, U.S.A.

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Time-dynamic profiling with application to hospital readmission among patients on dialysis.

Biometrics 2018 Jun 5. Epub 2018 Jun 5.

Department of Biostatistics, University of California, Los Angeles, California 90095, U.S.A.

Standard profiling analysis aims to evaluate medical providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. The outcome, for instance, may be mortality, medical complications, or 30-day (unplanned) hospital readmission. Profiling analysis involves regression modeling of a patient outcome, adjusting for patient health status at baseline, and comparing each provider's outcome rate (e. Read More

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Discussion on "time-dynamic profiling with application to hospital readmission among patients on dialysis," by Jason P. Estes, Danh V. Nguyen, Yanjun Chen, Lorien S. Dalrymple, Connie M. Rhee, Kamyar Kalantar-Zadeh, and Damla Senturk.

Authors:
Els Goetghebeur

Biometrics 2018 Jun 5. Epub 2018 Jun 5.

Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Krijgslaan 281-S9, 9000 Ghent, Belgium.

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Multiple imputation of missing data in nested case-control and case-cohort studies.

Biometrics 2018 Jun 5. Epub 2018 Jun 5.

Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K.

The nested case-control and case-cohort designs are two main approaches for carrying out a substudy within a prospective cohort. This article adapts multiple imputation (MI) methods for handling missing covariates in full-cohort studies for nested case-control and case-cohort studies. We consider data missing by design and data missing by chance. Read More

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A boxplot for circular data.

Biometrics 2018 May 21. Epub 2018 May 21.

Department of Economics and Law, University of Cassino and Southern Lazio, Italy.

The box-and-whiskers plot is an extraordinary graphical tool that provides a quick visual summary of an observed distribution. In spite of its many extensions, a really suitable boxplot to display circular data is not yet available. Thanks to its simplicity and strong visual impact, such a tool would be especially useful in all fields where circular measures arise: biometrics, astronomy, environmetrics, Earth sciences, to cite just a few. Read More

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May 2018
1 Read

Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data.

Biometrics 2018 May 18. Epub 2018 May 18.

Department of Statistics, North Carolina State University, Raleigh, North Carolina, U.S.A.

Clinicians often make multiple treatment decisions at key points over the course of a patient's disease. A dynamic treatment regime is a sequence of decision rules, each mapping a patient's observed history to the set of available, feasible treatment options at each decision point, and thus formalizes this process. An optimal regime is one leading to the most beneficial outcome on average if used to select treatment for the patient population. Read More

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Varying-coefficient semiparametric model averaging prediction.

Biometrics 2018 May 18. Epub 2018 May 18.

Department of Statistics and Applied Probability, National University of Singapore, Singapore 117546, Singapore.

Forecasting and predictive inference are fundamental data analysis tasks. Most studies employ parametric approaches making strong assumptions about the data generating process. On the other hand, while nonparametric models are applied, it is sometimes found in situations involving low signal to noise ratios or large numbers of covariates that their performance is unsatisfactory. Read More

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May 2018
2 Reads

Detection of multiple perturbations in multi-omics biological networks.

Biometrics 2018 May 17. Epub 2018 May 17.

Graduate Program in Bioinformatics, Boston University, Boston, U.S.A.

Cellular mechanism-of-action is of fundamental concern in many biological studies. It is of particular interest for identifying the cause of disease and learning the way in which treatments act against disease. However, pinpointing such mechanisms is difficult, due to the fact that small perturbations to the cell can have wide-ranging downstream effects. Read More

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May 2018
2 Reads

Semi-parametric methods of handling missing data in mortal cohorts under non-ignorable missingness.

Biometrics 2018 May 17. Epub 2018 May 17.

MRC Biostatistics Unit, University of Cambridge, IPH Forvie Site, Robinson Way, Cambridge CB2 0SR, U.K.

We propose semi-parametric methods to model cohort data where repeated outcomes may be missing due to death and non-ignorable dropout. Our focus is to obtain inference about the cohort composed of those who are still alive at any time point (partly conditional inference). We propose: i) an inverse probability weighted method that upweights observed subjects to represent subjects who are still alive but are not observed; ii) an outcome regression method that replaces missing outcomes of subjects who are alive with their conditional mean outcomes given past observed data; and iii) an augmented inverse probability method that combines the previous two methods and is double robust against model misspecification. Read More

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New robust statistical procedures for the polytomous logistic regression models.

Biometrics 2018 May 17. Epub 2018 May 17.

Department of Statistics, Complutense University of Madrid, 28040 Madrid, Spain.

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Read More

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Order selection and sparsity in latent variable models via the ordered factor LASSO.

Biometrics 2018 May 11. Epub 2018 May 11.

School of Mathematics and Statistics, and the Evolution & Ecology Research Centre, UNSW Sydney, NSW 2052, Australia.

Generalized linear latent variable models (GLLVMs) offer a general framework for flexibly analyzing data involving multiple responses. When fitting such models, two of the major challenges are selecting the order, that is, the number of factors, and an appropriate structure for the loading matrix, typically a sparse structure. Motivated by the application of GLLVMs to study marine species assemblages in the Southern Ocean, we propose the Ordered Factor LASSO or OFAL penalty for order selection and achieving sparsity in GLLVMs. Read More

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Doubly robust matching estimators for high dimensional confounding adjustment.

Biometrics 2018 May 11. Epub 2018 May 11.

RAND Corporation, Santa Monica, California 90401, U.S.A.

Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, then controlling for all available covariates is infeasible. In cases where a sparsity condition holds, variable selection or penalization can reduce the dimension of the covariate space in a manner that allows for valid estimation of treatment effects. Read More

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Sparse generalized eigenvalue problem with application to canonical correlation analysis for integrative analysis of methylation and gene expression data.

Biometrics 2018 May 11. Epub 2018 May 11.

Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, U.S.A.

We present a method for individual and integrative analysis of high dimension, low sample size data that capitalizes on the recurring theme in multivariate analysis of projecting higher dimensional data onto a few meaningful directions that are solutions to a generalized eigenvalue problem. We propose a general framework, called SELP (Sparse Estimation with Linear Programming), with which one can obtain a sparse estimate for a solution vector of a generalized eigenvalue problem. We demonstrate the utility of SELP on canonical correlation analysis for an integrative analysis of methylation and gene expression profiles from a breast cancer study, and we identify some genes known to be associated with breast carcinogenesis, which indicates that the proposed method is capable of generating biologically meaningful insights. Read More

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Exponential Family Functional data analysis via a low-rank model.

Biometrics 2018 May 8. Epub 2018 May 8.

Faculty of Business and Economics, University of Hong Kong, Hong Kong, China.

In many applications, non-Gaussian data such as binary or count are observed over a continuous domain and there exists a smooth underlying structure for describing such data. We develop a new functional data method to deal with this kind of data when the data are regularly spaced on the continuous domain. Our method, referred to as Exponential Family Functional Principal Component Analysis (EFPCA), assumes the data are generated from an exponential family distribution, and the matrix of the canonical parameters has a low-rank structure. Read More

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A powerful approach to the study of moderate effect modification in observational studies.

Biometrics 2018 May 8. Epub 2018 May 8.

Department of Statistics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A.

Effect modification means the magnitude or stability of a treatment effect varies as a function of an observed covariate. Generally, larger and more stable treatment effects are insensitive to larger biases from unmeasured covariates, so a causal conclusion may be considerably firmer if this pattern is noted if it occurs. We propose a new strategy, called the submax-method, that combines exploratory, and confirmatory efforts to determine whether there is stronger evidence of causality-that is, greater insensitivity to unmeasured confounding-in some subgroups of individuals. Read More

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Scalable Bayesian variable selection for structured high-dimensional data.

Biometrics 2018 May 8. Epub 2018 May 8.

Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.

Variable selection for structured covariates lying on an underlying known graph is a problem motivated by practical applications, and has been a topic of increasing interest. However, most of the existing methods may not be scalable to high-dimensional settings involving tens of thousands of variables lying on known pathways such as the case in genomics studies. We propose an adaptive Bayesian shrinkage approach which incorporates prior network information by smoothing the shrinkage parameters for connected variables in the graph, so that the corresponding coefficients have a similar degree of shrinkage. Read More

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Estimation of the optimal surrogate based on a randomized trial.

Biometrics 2018 Apr 27. Epub 2018 Apr 27.

Division of Biostatistics, University of California, Berkeley, California, 94720, U.S.A.

A common scientific problem is to determine a surrogate outcome for a long-term outcome so that future randomized studies can restrict themselves to only collecting the surrogate outcome. We consider the setting that we observe n independent and identically distributed observations of a random variable consisting of baseline covariates, a treatment, a vector of candidate surrogate outcomes at an intermediate time point, and the final outcome of interest at a final time point. We assume the treatment is randomized, conditional on the baseline covariates. Read More

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Using survival information in truncation by death problems without the monotonicity assumption.

Authors:
Fan Yang Peng Ding

Biometrics 2018 Apr 17. Epub 2018 Apr 17.

Department of Statistics, University of California, Berkeley, California 94720, U.S.A.

In some randomized clinical trials, patients may die before the measurement time point of their outcomes. Even though randomization generates comparable treatment and control groups, the remaining survivors often differ significantly in background variables that are prognostic to the outcomes. This is called the truncation by death problem. Read More

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April 2018
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On the analysis of discrete time competing risks data.

Biometrics 2018 Apr 17. Epub 2018 Apr 17.

Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, U.S.A.

Regression methodology has been well developed for competing risks data with continuous event times, both for the cause-specific hazard and cumulative incidence functions. However, in many applications, including those from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute, the event times may be observed discretely. Naive application of continuous time regression methods to such data is not appropriate. Read More

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April 2018
1 Read

Pseudo and conditional score approach to joint analysis of current count and current status data.

Biometrics 2018 Apr 17. Epub 2018 Apr 17.

Institute of Statistical Science, Academia Sinica, Taiwan.

We develop a joint analysis approach for recurrent and nonrecurrent event processes subject to case I interval censorship, which are also known in literature as current count and current status data, respectively. We use a shared frailty to link the recurrent and nonrecurrent event processes, while leaving the distribution of the frailty fully unspecified. Conditional on the frailty, the recurrent event is assumed to follow a nonhomogeneous Poisson process, and the mean function of the recurrent event and the survival function of the nonrecurrent event are assumed to follow some general form of semiparametric transformation models. Read More

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April 2018
1 Read

MILFM: Multiple index latent factor model based on high-dimensional features.

Biometrics 2018 Apr 17. Epub 2018 Apr 17.

Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A.

The aim of this article is to develop a multiple-index latent factor modeling (MILFM) framework to build an accurate prediction model for clinical outcomes based on a massive number of features. We develop a three-stage estimation procedure to build the prediction model. MILFM uses an independent screening method to select a set of informative features, which may have a complex nonlinear relationship with the outcome variables. Read More

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April 2018
1 Read

Nonparametric estimation of transition probabilities for a general progressive multi-state model under cross-sectional sampling.

Biometrics 2018 Mar 31. Epub 2018 Mar 31.

Department of Statistics, The Hebrew University of Jerusalem, Jerusalem 91905, Israel.

Nonparametric estimation of the transition probability matrix of a progressive multi-state model is considered under cross-sectional sampling. Two different estimators adapted to possibly right-censored and left-truncated data are proposed. The estimators require full retrospective information before the truncation time, which, when exploited, increases efficiency. Read More

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March 2018
1 Read

Sensitivity analysis and power for instrumental variable studies.

Biometrics 2018 Mar 31. Epub 2018 Mar 31.

The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.

In observational studies to estimate treatment effects, unmeasured confounding is often a concern. The instrumental variable (IV) method can control for unmeasured confounding when there is a valid IV. To be a valid IV, a variable needs to be independent of unmeasured confounders and only affect the outcome through affecting the treatment. Read More

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March 2018
1 Read

Mean residual life regression with functional principal component analysis on longitudinal data for dynamic prediction.

Biometrics 2018 Mar 30. Epub 2018 Mar 30.

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A.

Predicting patient life expectancy is of great importance for clinicians in making treatment decisions. This prediction needs to be conducted in a dynamic manner, based on longitudinal biomarkers repeatedly measured during the patient's post-treatment follow-up period. The prediction is updated any time a new biomarker measurement is obtained. Read More

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March 2018
2 Reads

Bayesian nonparametric generative models for causal inference with missing at random covariates.

Biometrics 2018 Mar 26. Epub 2018 Mar 26.

Department of Statistics, University of Florida, Gainesville, Florida 32611, U.S.A.

We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and confounders) is modeled using an enriched Dirichlet process. The combination of the observed data model and causal assumptions allows us to identify any type of causal effect-differences, ratios, or quantile effects, either marginally or for subpopulations of interest. Read More

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March 2018
1 Read

Power and sample size calculation incorporating misspecifications of the variance function in comparative clinical trials with over-dispersed count data.

Biometrics 2018 Mar 26. Epub 2018 Mar 26.

Department of Biostatistics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi 466-8550, Japan.

Over-dispersed count data are frequently observed in clinical trials where the primary endpoint is occurrence of clinical events. Sample sizes of comparative clinical trials with these data are typically calculated under negative binomial models or quasi-Poisson models with specified variance functions, or under the assumption that the specified "working" variance functions are correctly specified. In this article, we propose a sample size formula anticipating misspecifications of the working variance function. Read More

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March 2018
1 Read

A D-vine copula-based model for repeated measurements extending linear mixed models with homogeneous correlation structure.

Biometrics 2018 Mar 22. Epub 2018 Mar 22.

Zentrum Mathematik, Technische Universität München, Boltzmannstraße 3, 85748 Garching, Germany.

We propose a model for unbalanced longitudinal data, where the univariate margins can be selected arbitrarily and the dependence structure is described with the help of a D-vine copula. We show that our approach is an extremely flexible extension of the widely used linear mixed model if the correlation is homogeneous over the considered individuals. As an alternative to joint maximum-likelihood a sequential estimation approach for the D-vine copula is provided and validated in a simulation study. Read More

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March 2018
1 Read

Model-averaged confounder adjustment for estimating multivariate exposure effects with linear regression.

Biometrics 2018 Mar 22. Epub 2018 Mar 22.

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

In environmental and nutritional epidemiology and in many other fields, there is increasing interest in estimating the effect of simultaneous exposure to several agents (e.g., multiple nutrients, pesticides, or air pollutants) on a health outcome. Read More

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March 2018
3 Reads

A statistical model for helices with applications.

Biometrics 2018 Mar 22. Epub 2018 Mar 22.

Department of Statistics, University of Oxford, Oxford, UK.

Motivated by a cutting edge problem related to the shape of α-helices in proteins, we formulate a parametric statistical model, which incorporates the cylindrical nature of the helix. Our focus is to detect a "kink," which is a drastic change in the axial direction of the helix. We propose a statistical model for the straight α-helix and derive the maximum likelihood estimation procedure. Read More

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March 2018
1 Read

Report of the Editors-2017.

Authors:

Biometrics 2018 Mar;74(1):5-7

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March 2018
1 Read

Fast approximation of small p-values in permutation tests by partitioning the permutations.

Biometrics 2018 Mar 18;74(1):196-206. Epub 2017 May 18.

Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, Michigan 48109-2029, U.S.A.

Researchers in genetics and other life sciences commonly use permutation tests to evaluate differences between groups. Permutation tests have desirable properties, including exactness if data are exchangeable, and are applicable even when the distribution of the test statistic is analytically intractable. However, permutation tests can be computationally intensive. Read More

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March 2018
1 Read

Regularized continuous-time Markov Model via elastic net.

Biometrics 2018 Mar 13. Epub 2018 Mar 13.

Department of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, U.S.A.

Continuous-time Markov models are commonly used to analyze longitudinal transitions between multiple disease states in panel data, where participants' disease states are only observed at multiple time points, and the exact state paths between observations are unknown. However, when covariate effects are incorporated and allowed to vary for different transitions, the number of potential parameters to estimate can become large even when the number of covariates is moderate, and traditional maximum likelihood estimation and subset model selection procedures can easily become unstable due to overfitting. We propose a novel regularized continuous-time Markov model with the elastic net penalty, which is capable of simultaneous variable selection and estimation for large number of parameters. Read More

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March 2018
1 Read

Motivating sample sizes in adaptive Phase I trials via Bayesian posterior credible intervals.

Authors:
Thomas M Braun

Biometrics 2018 Mar 13. Epub 2018 Mar 13.

Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, U.S.A.

In contrast with typical Phase III clinical trials, there is little existing methodology for determining the appropriate numbers of patients to enroll in adaptive Phase I trials. And, as stated by Dennis Lindley in a more general context, "[t]he simple practical question of 'What size of sample should I take' is often posed to a statistician, and it is a question that is embarrassingly difficult to answer." Historically, simulation has been the primary option for determining sample sizes for adaptive Phase I trials, and although useful, can be problematic and time-consuming when a sample size is needed relatively quickly. Read More

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March 2018
1 Read

Estimating individualized treatment rules for ordinal treatments.

Biometrics 2018 Mar 13. Epub 2018 Mar 13.

Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.

Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been proposed recently. One of the primary goals of precision medicine is to obtain an optimal individual treatment rule (ITR), which can help make decisions on treatment selection according to each patient's specific characteristics. Read More

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March 2018
1 Read

A group sequential test for treatment effect based on the Fine-Gray model.

Biometrics 2018 Mar 13. Epub 2018 Mar 13.

Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, U.S.A.

Competing risks endpoints arise when patients can fail therapy from several causes. Analyzing these outcomes allows one to assess directly the benefit of treatment on a primary cause of failure in a clinical trial setting. Regression models can be used in clinical trials to adjust for residual imbalances in patient characteristics, improving the power to detect treatment differences. Read More

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March 2018
1 Read

Model selection for semiparametric marginal mean regression accounting for within-cluster subsampling variability and informative cluster size.

Biometrics 2018 Mar 13. Epub 2018 Mar 13.

Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, R.O.C.

We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is "informative" in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within-cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121-1134) and accommodates informative cluster size. Read More

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March 2018
2 Reads

An approximate joint model for multiple paired longitudinal outcomes and time-to-event data.

Biometrics 2018 Feb 28. Epub 2018 Feb 28.

Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland 20850, U.S.A.

Joint modeling of multivariate paired longitudinal data and time-to-event data presents computational challenges that supersede full likelihood estimation due to the large dimensional random effects vector needed to capture correlation due to clustering with respect to pairs, subjects, and outcomes. We propose an alternative, computationally simpler approach to estimation of complex shared parameter models where missing data is imputed based on the Posterior Predictive Distribution from a Conditional Linear Model (CLM) approximation. Existing methods for complete data are then implemented to obtain estimates of the event time model parameters. Read More

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February 2018
1 Read

A Bayesian nonparametric approach to causal inference on quantiles.

Biometrics 2018 Feb 25. Epub 2018 Feb 25.

Department of Pharmaceutical Outcomes & Policy, Department of Epidemiology, University of Florida, Florida 32601, U.S.A.

We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. Read More

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February 2018
2 Reads

Bayesian enhancement two-stage design for single-arm phase II clinical trials with binary and time-to-event endpoints.

Biometrics 2018 Feb 21. Epub 2018 Feb 21.

Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong.

Simon's two-stage design is one of the most commonly used methods in phase II clinical trials with binary endpoints. The design tests the null hypothesis that the response rate is less than an uninteresting level, versus the alternative hypothesis that the response rate is greater than a desirable target level. From a Bayesian perspective, we compute the posterior probabilities of the null and alternative hypotheses given that a promising result is declared in Simon's design. Read More

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February 2018
1 Read

A wild bootstrap approach for the Aalen-Johansen estimator.

Biometrics 2018 Feb 16. Epub 2018 Feb 16.

Institute of Statistics, Ulm University, Ulm, Germany.

We suggest a wild bootstrap resampling technique for nonparametric inference on transition probabilities in a general time-inhomogeneous Markov multistate model. We first approximate the limiting distribution of the Nelson-Aalen estimator by repeatedly generating standard normal wild bootstrap variates, while the data is kept fixed. Next, a transformation using a functional delta method argument is applied. Read More

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February 2018
1 Read