Journal of Probability and Statistics

New Advances in Biostatistics


Publishing date
01 Dec 2018
Status
Published
Submission deadline
27 Jul 2018

Lead Editor

1Georgia State University, Atlanta, USA

2Auburn University, Auburn, USA

3University of California, Davis, USA

4Purdue University, West Lafayette, USA

5University of Texas Health Science Center, Houston, USA


New Advances in Biostatistics

Description

Biostatistics deals with data arising from biomedical research. It remains a very active research area with complicated time-to-event data and missing data emerging in application areas including medicine, genetics, neuroscience, and engineering. Recent advances in biomedical research have created new challenges and opportunities for statistics researchers and data scientists. For example, big data analysis, precision medicine, artificial intelligence, causal inference, and other new research fields have inspired data scientists to develop modern statistical methods and innovative inference procedures. This special issue aims to provide a forum for researchers to present new methods and novel applications motivated by biomedical examples in the broad areas of contemporary biostatistics. Authors are invited to submit original research and review articles that will stimulate interest in contemporary biostatistics.

Potential topics include but are not limited to the following:

  • Missing data analysis
  • Precision medicine
  • Clinical trials
  • Causal inference
  • Longitudinal data analysis
  • Survival analysis
  • High dimensional data analysis and big data analysis
  • fMRI data analysis
  • Health care data analysis
  • Neuroscience data analysis
  • Functional data analysis

Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 1352310
  • - Editorial

New Advances in Biostatistics

Yichuan Zhao | Ash Abebe | ... | Xu Zhang
  • Special Issue
  • - Volume 2019
  • - Article ID 7173416
  • - Research Article

Improved Small Sample Inference on the Ratio of Two Coefficients of Variation of Two Independent Lognormal Distributions

A. Wong | L. Jiang
  • Special Issue
  • - Volume 2019
  • - Article ID 9750538
  • - Review Article

A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data

Michelle L. Vidoni | Belinda M. Reininger | MinJae Lee
  • Special Issue
  • - Volume 2019
  • - Article ID 8953530
  • - Research Article

On the Use of Min-Max Combination of Biomarkers to Maximize the Partial Area under the ROC Curve

Hua Ma | Susan Halabi | Aiyi Liu
  • Special Issue
  • - Volume 2019
  • - Article ID 8057820
  • - Research Article

Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network

Xiaoling Wei | Jimin Li | ... | Xiuling Liu
  • Special Issue
  • - Volume 2018
  • - Article ID 2834183
  • - Research Article

A Note on the Adaptive LASSO for Zero-Inflated Poisson Regression

Prithish Banerjee | Broti Garai | ... | Saptarshi Chatterjee
  • Special Issue
  • - Volume 2018
  • - Article ID 3506794
  • - Research Article

Detecting Spatial Clusters via a Mixture of Dirichlet Processes

Meredith A. Ray | Jian Kang | Hongmei Zhang
  • Special Issue
  • - Volume 2018
  • - Article ID 8654173
  • - Research Article

A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate

Márcio Augusto Diniz | Sungjin Kim | Mourad Tighiouart
  • Special Issue
  • - Volume 2018
  • - Article ID 1581979
  • - Review Article

Mixed Effects Models with Censored Covariates, with Applications in HIV/AIDS Studies

Lang Wu | Hongbin Zhang
Journal of Probability and Statistics
 Journal metrics
Acceptance rate9%
Submission to final decision41 days
Acceptance to publication29 days
CiteScore0.700
Impact Factor-
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