Biometrics Journal Impact Factor & Information

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

Journal description

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.

Current impact factor: 1.52

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 1.521
2012 Impact Factor 1.412
2011 Impact Factor 1.827
2010 Impact Factor 1.764
2009 Impact Factor 1.867
2008 Impact Factor 1.97
2007 Impact Factor 1.714
2006 Impact Factor 1.489
2005 Impact Factor 1.602
2004 Impact Factor 1.211
2003 Impact Factor 1.324
2002 Impact Factor 1.077
2001 Impact Factor 1.081
2000 Impact Factor 1.17
1999 Impact Factor 1.335
1998 Impact Factor 0.863
1997 Impact Factor 0.938
1996 Impact Factor 1.011
1995 Impact Factor 1.041
1994 Impact Factor 1.207
1993 Impact Factor 0.97
1992 Impact Factor 1.027

Impact factor over time

Impact factor
Year

Additional details

5-year impact 2.01
Cited half-life 0.00
Immediacy index 0.21
Eigenfactor 0.02
Article influence 1.55
Website Biometrics website
Other titles Biometrics
ISSN 0006-341X
OCLC 5898885
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

Wiley

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Some journals have separate policies, please check with each journal directly
    • On author's personal website, institutional repositories, arXiv, AgEcon, PhilPapers, PubMed Central, RePEc or Social Science Research Network
    • Author's pre-print may not be updated with Publisher's Version/PDF
    • Author's pre-print must acknowledge acceptance for publication
    • On a non-profit server
    • Publisher's version/PDF cannot be used
    • Publisher source must be acknowledged with citation
    • Must link to publisher version with set statement (see policy)
    • This policy is an exception to the default policies of 'Wiley'
  • Classification
    ​ green

Publications in this journal

  • Biometrics 06/2015; 71(2). DOI:10.1111/biom.12320
  • Biometrics 06/2015; 71(2). DOI:10.1111/biom.12321
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    ABSTRACT: EDITOR: TAESUNG PARK Clinical Trials with Missing Data (M. O'Kelly and B. Ratitch) Juwon Song Multiple Imputation and its Application (J. R. Carpenter and M. G. Kenward) Sohee Park Statistical Inference on Residual Life (J.-H. Jeong) Seungyeoun Lee
    Biometrics 06/2015; 71(2). DOI:10.1111/biom.12319
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    ABSTRACT: This article develops a Bayesian semiparametric approach to the extended hazard model, with generalization to high-dimensional spatially grouped data. County-level spatial correlation is accommodated marginally through the normal transformation model of Li and Lin (2006, Journal of the American Statistical Association 101, 591-603), using a correlation structure implied by an intrinsic conditionally autoregressive prior. Efficient Markov chain Monte Carlo algorithms are developed, especially applicable to fitting very large, highly censored areal survival data sets. Per-variable tests for proportional hazards, accelerated failure time, and accelerated hazards are efficiently carried out with and without spatial correlation through Bayes factors. The resulting reduced, interpretable spatial models can fit significantly better than a standard additive Cox model with spatial frailties. © 2014, The International Biometric Society.
    Biometrics 12/2014; 71(2). DOI:10.1111/biom.12268
  • Biometrics 12/2014; 70(4):1062-1062. DOI:10.1111/biom.12262
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    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; 71(1). DOI:10.1111/biom.12252
  • Source
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    ABSTRACT: Electronic health record (EHR) data are becoming an increasingly common data source for understanding clinical risk of acute events. While their longitudinal nature presents opportunities to observe changing risk over time, these analyses are complicated by the sparse and irregular measurements of many of the clinical metrics making typical statistical methods unsuitable for these data. In this paper, we present an analytic procedure to both sample from an EHR and analyze the data to detect clinically meaningful markers of acute myocardial infarction (MI). Using an EHR from a large national dialysis organization we abstracted the records of 64,318 individuals and identified 5,314 people that had an MI during the study period. We describe a nested case-control design to sample appropriate controls and an analytic approach using regression splines. Fitting a mixed-model with truncated power splines we perform a series of goodness-of-fit tests to determine whether any of 11 regularly collected laboratory markers are useful clinical predictors. We test the clinical utility of each marker using an independent test set. The results suggest that EHR data can be easily used to detect markers of clinically acute events. Special software or analytic tools are not needed, even with irregular EHR data.
    Biometrics 07/2014; 71(2). DOI:10.1111/biom.12283
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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; 70(4). DOI:10.1111/biom.12198
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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; 70(4). DOI:10.1111/biom.12207
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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; 70(4). DOI:10.1111/biom.12206
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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; 70(4). DOI:10.1111/biom.12193
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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; 70(3). DOI:10.1111/biom.12199
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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; 70(3). DOI:10.1111/biom.12201
  • Biometrics 06/2014; 70(3). DOI:10.1111/biom.12187