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Mathematics
Special Issues
New Developments in Statistical Design and Analysis of Clinical Trials
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New Developments in Statistical Design and Analysis of Clinical Trials

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 5043

Special Issue Editors

Dr. Xianming Tan

E-Mail Website
Guest Editor
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Interests: safety data analysis; adaptive designs; Bayesian statistics in clinical trials; longitudinal multivariate data analysis; causal inference
Dr. Jianping Sun

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, University of North Carolina at Greensboro, Greensboro, NC 27412, USA
Interests: safety data analysis; statistical genetics; composite likelihood

Special Issue Information

Dear Colleagues,

We cordially invite you to contribute to a Special Issue on "New Developments in Statistical Design and Analysis of Clinical Trials" with an original research article or comprehensive review. The submissions will be evaluated through the peer-review system of Mathematics.

Clinical trials play a pivotal role in advancing medical knowledge and patient care. As the complexity of clinical research grows, so does the need for innovative statistical designs and analytical methods to ensure the validity and efficiency of trial results. Such developments can help researchers to make better decisions during the trial process, reduce the required sample sizes, and improve the overall quality of evidence generated from clinical trials.

The focus of this Special Issue is on new and innovative statistical methodologies for the design and analysis of clinical trials. Topics of interest include but are not limited to:

  • Novel trial designs, such as adaptive and Bayesian designs;
  • Methods for handling missing data and informative censoring;
  • Techniques for analyzing high-dimensional and complex data structures;
  • Innovations in the evaluation of treatment effects, including causal inference and subgroup analysis;
  • Approaches for addressing multiplicity and interim analyses;
  • Statistical methods for monitoring safety and efficacy during the trial.

By showcasing the latest advancements in statistical design and analysis of clinical trials, this Special Issue aims to promote the development and application of cutting-edge methodologies in clinical research, ultimately benefiting patients and the broader medical community.

We look forward to receiving your valuable contributions and fostering insightful discussions on this important topic.

Dr. Xianming Tan
Dr. Jianping Sun
Guest Editors

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 trial
  • safety data
  • adaptive design
  • causal inference
  • subgroup analysis
  • longitudinal multivariate data

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

19 pages, 1371 KiB  
Article
Improved Apriori Method for Safety Signal Detection Using Post-Marketing Clinical Data
by Reetika Sarkar and Jianping Sun
Mathematics 2024, 12(17), 2705; https://doi.org/10.3390/math12172705 - 30 Aug 2024
Cited by 1 | Viewed by 989
Abstract
Safety signal detection is an integral component of Pharmacovigilance (PhV), which is defined by the World Health Organization as “science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other possible drug related problems”. The purpose of [...] Read more.
Safety signal detection is an integral component of Pharmacovigilance (PhV), which is defined by the World Health Organization as “science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other possible drug related problems”. The purpose of safety signal detection is to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. While post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals, there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS) demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data. Full article
(This article belongs to the Special Issue New Developments in Statistical Design and Analysis of Clinical Trials)
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15 pages, 1136 KiB  
Article
Accounting for Measurement Error and Untruthfulness in Binary RRT Models
by Bailey Meche, Venu Poruri, Sat Gupta and Sadia Khalil
Mathematics 2024, 12(6), 875; https://doi.org/10.3390/math12060875 - 16 Mar 2024
Viewed by 993
Abstract
This study examines the effect of measurement error on binary Randomized Response Technique models. We discuss a method for estimating and accounting for measurement error and untruthfulness in two basic models and one comprehensive model. Both theoretical and empirical results show that not [...] Read more.
This study examines the effect of measurement error on binary Randomized Response Technique models. We discuss a method for estimating and accounting for measurement error and untruthfulness in two basic models and one comprehensive model. Both theoretical and empirical results show that not accounting for measurement error leads to inaccurate estimates. We introduce estimators that account for the effect of measurement error. Furthermore, we introduce a new measure of model privacy using an odds ratio statistic, which offers better interpretability than traditional methods. Full article
(This article belongs to the Special Issue New Developments in Statistical Design and Analysis of Clinical Trials)
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18 pages, 741 KiB  
Article
Novel Design and Analysis for Rare Disease Drug Development
by Shein Chung Chow, Annpey Pong and Susan S. Chow
Mathematics 2024, 12(5), 631; https://doi.org/10.3390/math12050631 - 21 Feb 2024
Cited by 4 | Viewed by 2203
Abstract
For rare disease drug development, the United States (US) Food and Drug Administration (FDA) has indicated that the same standards as those for drug products for common conditions will be applied. To assist the sponsors in rare disease drug development, the FDA has [...] Read more.
For rare disease drug development, the United States (US) Food and Drug Administration (FDA) has indicated that the same standards as those for drug products for common conditions will be applied. To assist the sponsors in rare disease drug development, the FDA has initiated several incentive programs to encourage the sponsors in rare disease drug development. In practice, these incentive programs may not help in achieving the study objectives due to the limited small patient population. To overcome this problem, some out-of-the-box innovative thinking and/or approaches, without jeopardizing the integrity, quality, and scientific validity of rare disease drug development, are necessarily considered. These innovative thinking and/or approaches include but are not limited to (i) sample size justification based on probability statements rather than conventional power analysis; (ii) demonstrating not-ineffectiveness and not-unsafeness rather than demonstrating effectiveness and safety with the small patient population (i.e., limited sample size) available; (iii) the use of complex innovative designs such as a two-stage seamless adaptive trial design and/or an n-of-1 trial design for flexibility and the efficient assessment of the test treatment under study; (iv) using real-world data (RWD) and real-world evidence (RWE) to support regulatory submission; and (v) conducting an individual benefit–risk assessment for a complete picture of the clinical performance of the test treatment under investigation. In this article, we provide a comprehensive summarization of this innovative thinking and these approaches for an efficient, accurate and reliable assessment of a test treatment used for treating patients with rare diseases under study. Statistical considerations including challenges and justifications are provided whenever possible. In addition, an innovative approach that combines innovative thinking and these approaches is proposed for regulatory consideration in rare disease drug development. Full article
(This article belongs to the Special Issue New Developments in Statistical Design and Analysis of Clinical Trials)
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