Biometrics (BIOMETRICS )

Publisher: American Statistical Association. Biometrics Section; Biometric Society; International Biometric Society, Blackwell Publishing


Biometrics is published quarterly. Its general objects are to promote and extend the use of mathematical and statistical methods in various subject-matter disciplines, by describing and exemplifying developments in these methods and their application in a form readily assimilable by experimenters and those concerned primarily with analysis of data. The journal is a ready medium for publication of papers by both the experimentalist and the statistician. The papers in the journal include statistical, authoritative expository or review articles, and analytical or methodological papers contributing to the planning or analysis of experiments and surveys, or the interpretation of data. Many of the papers in Biometrics contain worked examples of the statistical analyses proposed.

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    Biometrics website
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Blackwell Publishing

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    • Some journals impose embargoes typically of 6 or 12 months, occasionally of 24 months
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    • Articles in some journals can be made Open Access on payment of additional charge
    • 'Blackwell Publishing' is an imprint of 'Wiley-Blackwell'
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Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Multistate models are used to characterize individuals' natural histories through diseases with discrete states. Observational data resources based on electronic medical records pose new opportunities for studying such diseases. However, these data consist of observations of the process at discrete sampling times, which may either be pre-scheduled and non-informative, or symptom-driven and informative about an individual's underlying disease status. We have developed a novel joint observation and disease transition model for this setting. The disease process is modeled according to a latent continuous-time Markov chain; and the observation process, according to a Markov-modulated Poisson process with observation rates that depend on the individual's underlying disease status. The disease process is observed at a combination of informative and non-informative sampling times, with possible misclassification error. We demonstrate that the model is computationally tractable and devise an expectation-maximization algorithm for parameter estimation. Using simulated data, we show how estimates from our joint observation and disease transition model lead to less biased and more precise estimates of the disease rate parameters. We apply the model to a study of secondary breast cancer events, utilizing mammography and biopsy records from a sample of women with a history of primary breast cancer.
    Biometrics 10/2014;
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    ABSTRACT: There has been an increasing interest in the analysis of spatially distributed multivariate binary data motivated by a wide range of research problems. Two types of correlations are usually involved: the correlation between the multiple outcomes at one location and the spatial correlation between the locations for one particular outcome. The commonly used regression models only consider one type of correlations while ignoring or modeling inappropriately the other one. To address this limitation, we adopt a Bayesian nonparametric approach to jointly modeling multivariate spatial binary data by integrating both types of correlations. A multivariate probit model is employed to link the binary outcomes to Gaussian latent variables; and Gaussian processes are applied to specify the spatially correlated random effects. We develop an efficient Markov chain Monte Carlo algorithm for the posterior computation. We illustrate the proposed model on simulation studies and a multidrug-resistant tuberculosis case study.
    Biometrics 06/2014;
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    ABSTRACT: Research in the field of nonparametric shape constrained regression has been intensive. However, only few publications explicitly deal with unimodality although there is need for such methods in applications, for example, in dose-response analysis. In this article, we propose unimodal spline regression methods that make use of Bernstein-Schoenberg splines and their shape preservation property. To achieve unimodal and smooth solutions we use penalized splines, and extend the penalized spline approach toward penalizing against general parametric functions, instead of using just difference penalties. For tuning parameter selection under a unimodality constraint a restricted maximum likelihood and an alternative Bayesian approach for unimodal regression are developed. We compare the proposed methodologies to other common approaches in a simulation study and apply it to a dose-response data set. All results suggest that the unimodality constraint or the combination of unimodality and a penalty can substantially improve estimation of the functional relationship.
    Biometrics 06/2014;
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    ABSTRACT: Integrative genomics offers a promising approach to more powerful genetic association studies. The hope is that combining outcome and genotype data with other types of genomic information can lead to more powerful SNP detection. We present a new association test based on a statistical model that explicitly assumes that genetic variations affect the outcome through perturbing gene expression levels. It is shown analytically that the proposed approach can have more power to detect SNPs that are associated with the outcome through transcriptional regulation, compared to tests using the outcome and genotype data alone, and simulations show that our method is relatively robust to misspecification. We also provide a strategy for applying our approach to high-dimensional genomic data. We use this strategy to identify a potentially new association between a SNP and a yeast cell's response to the natural product tomatidine, which standard association analysis did not detect.
    Biometrics 06/2014;
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    ABSTRACT: We develop a linear mixed regression model where both the response and the predictor are functions. Model parameters are estimated by maximizing the log likelihood via the ECME algorithm. The estimated variance parameters or covariance matrices are shown to be positive or positive definite at each iteration. In simulation studies, the approach outperforms in terms of the fitting error and the MSE of estimating the "regression coefficients."
    Biometrics 06/2014;
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    ABSTRACT: We consider the problem of robust estimation of the regression relationship between a response and a covariate based on sample in which precise measurements on the covariate are not available but error-prone surrogates for the unobserved covariate are available for each sampled unit. Existing methods often make restrictive and unrealistic assumptions about the density of the covariate and the densities of the regression and the measurement errors, for example, normality and, for the latter two, also homoscedasticity and thus independence from the covariate. In this article we describe Bayesian semiparametric methodology based on mixtures of B-splines and mixtures induced by Dirichlet processes that relaxes these restrictive assumptions. In particular, our models for the aforementioned densities adapt to asymmetry, heavy tails and multimodality. The models for the densities of regression and measurement errors also accommodate conditional heteroscedasticity. In simulation experiments, our method vastly outperforms existing methods. We apply our method to data from nutritional epidemiology.
    Biometrics 06/2014;
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    ABSTRACT: Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this article, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales.
    Biometrics 06/2014;
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    ABSTRACT: Spatial-clustered data refer to high-dimensional correlated measurements collected from units or subjects that are spatially clustered. Such data arise frequently from studies in social and health sciences. We propose a unified modeling framework, termed as GeoCopula, to characterize both large-scale variation, and small-scale variation for various data types, including continuous data, binary data, and count data as special cases. To overcome challenges in the estimation and inference for the model parameters, we propose an efficient composite likelihood approach in that the estimation efficiency is resulted from a construction of over-identified joint composite estimating equations. Consequently, the statistical theory for the proposed estimation is developed by extending the classical theory of the generalized method of moments. A clear advantage of the proposed estimation method is the computation feasibility. We conduct several simulation studies to assess the performance of the proposed models and estimation methods for both Gaussian and binary spatial-clustered data. Results show a clear improvement on estimation efficiency over the conventional composite likelihood method. An illustrative data example is included to motivate and demonstrate the proposed method.
    Biometrics 06/2014;
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    ABSTRACT: Follow-up is more and more used in medicine/doping control to identify abnormal results in an individual. Currently, follow-ups are mostly carried out variable by variable using "reference intervals" that contain the values observable in 100(1-α)% of healthy/undoped individuals. Observations of the evolution of the variables over time in a sample of N healthy/undoped individuals, allows these reference intervals to be individualized by taking into account the possible effect of covariables and some previous observations of these variables obtained when the individual was healthy/undoped. For each variable these individualized intervals should contain 100(1-α)% of observable values compatible with previous observed values in this individual. General methods to build these intervals are available, but they allow only a variable by variable follow-up whatever the possible correlations over time between them. In this article, we propose a general method to jointly follow-up several correlated variables over time. This methodology relies on a multivariate linear mixed effects model. We first provide a method to estimate the model's parameters. In an asymptotic framework (N large enough), we then derive a (1-α) individualized prediction region. Sometimes, the sample size N is not large enough for the asymptotic framework to give a reasonable prediction region. It is for this reason, we propose and compare three different prediction regions that should behave better for small N. Finally, the whole methodology is illustrated by the follow-up of kidney insufficiency in cats.
    Biometrics 06/2014;
  • Biometrics 06/2014;
  • Biometrics 06/2014;
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    ABSTRACT: Kang, Janes and Huang propose an interesting boosting method to combine biomarkers for treatment selection. The method requires modeling the treatment effects using markers. We discuss an alternative method, outcome weighted learning. This method sidesteps the need for modeling the outcomes, and thus can be more robust to model misspecification.
    Biometrics 05/2014;
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    ABSTRACT: Markers that predict treatment effect have the potential to improve patient outcomes. For example, the OncotypeDX® RecurrenceScore® has some ability to predict the benefit of adjuvant chemotherapy over and above hormone therapy for the treatment of estrogen-receptor-positive breast cancer, facilitating the provision of chemotherapy to women most likely to benefit from it. Given that the score was originally developed for predicting outcome given hormone therapy alone, it is of interest to develop alternative combinations of the genes comprising the score that are optimized for treatment selection. However, most methodology for combining markers is useful when predicting outcome under a single treatment. We propose a method for combining markers for treatment selection which requires modeling the treatment effect as a function of markers. Multiple models of treatment effect are fit iteratively by upweighting or "boosting" subjects potentially misclassified according to treatment benefit at the previous stage. The boosting approach is compared to existing methods in a simulation study based on the change in expected outcome under marker-based treatment. The approach improves upon methods in some settings and has comparable performance in others. Our simulation study also provides insights as to the relative merits of the existing methods. Application of the boosting approach to the breast cancer data, using scaled versions of the original markers, produces marker combinations that may have improved performance for treatment selection.
    Biometrics 05/2014;
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    ABSTRACT: The log-rank test has been widely used to test treatment effects under the Cox model for censored time-to-event outcomes, though it may lose power substantially when the model's proportional hazards assumption does not hold. In this article, we consider an extended Cox model that uses B-splines or smoothing splines to model a time-varying treatment effect and propose score test statistics for the treatment effect. Our proposed new tests combine statistical evidence from both the magnitude and the shape of the time-varying hazard ratio function, and thus are omnibus and powerful against various types of alternatives. In addition, the new testing framework is applicable to any choice of spline basis functions, including B-splines, and smoothing splines. Simulation studies confirm that the proposed tests performed well in finite samples and were frequently more powerful than conventional tests alone in many settings. The new methods were applied to the HIVNET 012 Study, a randomized clinical trial to assess the efficacy of single-dose Nevirapine against mother-to-child HIV transmission conducted by the HIV Prevention Trial Network.
    Biometrics 05/2014;
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    ABSTRACT: A primary objective in the application of postmarketing drug safety surveillance is to ascertain the relationship between time-varying drug exposures and recurrent adverse events (AEs) related to health outcomes. The self-controlled case series (SCCS) method is one approach to analysis in this context. It is based on a conditional Poisson regression model, which assumes that events at different time points are conditionally independent given the covariate process. This requirement is problematic when the occurrence of an event can alter the future event risk. In a clinical setting, for example, patients who have a first myocardial infarction (MI) may be at higher subsequent risk for a second. In this work, we propose the positive dependence s elf-controlled case senes (PD-SCCS) method: a generalization of SCCS that allows the occurrence of an event to increase the future event risk, yet maintains the advantages of the original model by controlling for fixed baseline covariates and relying solely on data from cases. As in the SCCS model, individual-level baseline parameters drop out of the PD-SCCS likelihood. Data sources used for postmarketing surveillance can contain tens of millions of people, so in this situation it is particularly advantageous that PD-SCCS avoids doing a costly estimation of individual parameters. We develop expressions for large sample inference and optimization for PD-SCCS and compare the results of our generalized model with the more restrictive SCCS approach.
    Biometrics 01/2013; 69(1):128-136.
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    ABSTRACT: Many animal monitoring studies seek to estimate the proportion of a study area occupied by a target population. The study area is divided into spatially distinct sites where the detected presence or absence of the population is recorded, and this is repeated in time for multiple seasons. However, when occupied sites are detected with probability p < 1, the lack of a detection does not imply lack of occupancy. MacKenzie et al. (2003, Ecology 84, 2200-2207) developed a multiseason model for estimating seasonal site occupancy (ψ t ) while accounting for unknown p. Their model performs well when observations are collected according to the robust design, where multiple sampling occasions occur during each season; the repeated sampling aids in the estimation p. However, their model does not perform as well when the robust design is lacking. In this paper, we propose an alternative likelihood model that yields improved seasonal estimates of p and ψ t in the absence of the robust design. We construct the marginal likelihood of the observed data by conditioning on, and summing out, the latent number of occupied sites during each season. A simulation study shows that in cases without the robust design, the proposed model estimates p with less bias than the MacKenzie et al. model and hence improves the estimates of ψ t . We apply both models to a data set consisting of repeated presence-absence observations of American robins (Turdus migratonus) with yearly survey periods. The two models are compared to a third estimator available when the repeated counts (from the same study) are considered, with the proposed model yielding estimates of ψ t closest to estimates from the point count model.
    Biometrics 01/2013; 69(1):146-156.