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Mathematics
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Recent Development in Biostatistics and Health Science
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Recent Development in Biostatistics and Health Science

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2339

Special Issue Editor

Prof. Dr. Ao Yuan

E-Mail Website
Guest Editor
Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC 20057, USA
Interests: statistics; biostatistics

Special Issue Information

Dear Colleagues,

The mathematical modeling of real-world problems leads to the development of new methods in biostatistics and health science, including causal inference in clinical trials, functional data analysis, subgroup analysis, integrative analysis, precision medicine, multi-regional analysis, statistical genetics, etc. Applicative areas include health sciences, statistics, biostatistics, economics, engineering, etc.

The theoretical and computational aspects of these topics call for innovative methods and efficient algorithms for them to be of practical value.

This Special Issue collects papers with the aim of motivating and exploiting innovative methodologies in various applicative areas. Special attention is devoted to the development of cutting-edge new methods and fast algorithms for problems in biostatistics and health science.

Prof. Dr. Ao Yuan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • clinical trials
  • causal inference
  • functional data analysis
  • subgroup analysis
  • multi-regional analysis
  • integrative analysis
  • precision medicine
  • statistical genetics

 

Published Papers (2 papers)

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Research

14 pages, 767 KiB  
Article
Stochastic EM Algorithm for Joint Model of Logistic Regression and Mechanistic Nonlinear Model in Longitudinal Studies
by Hongbin Zhang
Mathematics 2023, 11(10), 2317; https://doi.org/10.3390/math11102317 - 16 May 2023
Viewed by 846
Abstract
We study a joint model where logistic regression is applied to binary longitudinal data with a mismeasured time-varying covariate that is modeled using a mechanistic nonlinear model. Multiple random effects are necessary to characterize the trajectories of the covariate and the response variable, [...] Read more.
We study a joint model where logistic regression is applied to binary longitudinal data with a mismeasured time-varying covariate that is modeled using a mechanistic nonlinear model. Multiple random effects are necessary to characterize the trajectories of the covariate and the response variable, leading to a high dimensional integral in the likelihood. To account for the computational challenge, we propose a stochastic expectation-maximization (StEM) algorithm with a Gibbs sampler coupled with Metropolis–Hastings sampling for the inference. In contrast with previous developments, this algorithm uses single imputation of the missing data during the Monte Carlo procedure, substantially increasing the computing speed. Through simulation, we assess the algorithm’s convergence and compare the algorithm with more classical approaches for handling measurement errors. We also conduct a real-world data analysis to gain insights into the association between CD4 count and viral load during HIV treatment. Full article
(This article belongs to the Special Issue Recent Development in Biostatistics and Health Science)
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17 pages, 895 KiB  
Article
Estimation and Hypothesis Test for Mean Curve with Functional Data by Reproducing Kernel Hilbert Space Methods, with Applications in Biostatistics
by Ming Xiong, Ao Yuan, Hong-Bin Fang, Colin O. Wu and Ming T. Tan
Mathematics 2022, 10(23), 4549; https://doi.org/10.3390/math10234549 - 01 Dec 2022
Cited by 1 | Viewed by 1070
Abstract
Functional data analysis has important applications in biomedical, health studies and other areas. In this paper, we develop a general framework for a mean curve estimation for functional data using a reproducing kernel Hilbert space (RKHS) and derive its asymptotic distribution theory. We [...] Read more.
Functional data analysis has important applications in biomedical, health studies and other areas. In this paper, we develop a general framework for a mean curve estimation for functional data using a reproducing kernel Hilbert space (RKHS) and derive its asymptotic distribution theory. We also propose two statistics for testing the equality of mean curves from two populations and a mean curve belonging to some subspace, respectively. Simulation studies are conducted to evaluate the performance of the proposed method and are compared with the major existing methods, which shows that the proposed method has a better performance than the existing ones. The method is then illustrated with an analysis of the growth data from the National Growth and Health Study (NGHS) project sponsored by the NIH. Full article
(This article belongs to the Special Issue Recent Development in Biostatistics and Health Science)
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