Special Issue "Applied Medical Statistics: Theory, Computation, Applicability"
Deadline for manuscript submissions: 30 June 2021.
Interests: applied and computational statistics; molecular modeling; genetic analysis; statistical modeling in medicine; integrated health informatics system; medical diagnostic research; statistical inference; medical imaging analysis; assisted decision systems; research ethics; social media and health information; evidence-based medicine
Special Issues and Collections in MDPI journals
Interests: epidemiology; Bayesian latent class models for the evaluation of diagnostic tests; prevalence estimation and proof of disease freedom; deep learning applications in diagnostics
Scientific knowledge in medicine is supported by a reproducible design of experiment and a valid and reliable statistical analysis. Both components are a ‘must’ since scientific research in medicine aims to improve medical outcomes. Statistical methods need proper understanding by researchers as well as readers to produce the expected outcome, namely successful medical diagnosis and treatment. Considerable progress in medical statistics was made during the last decade, and many methods exist but they are too complex to be easily understood by physicians.
The purpose of the Applied Medical Statistics: Theory, Computation, Applicability Special Issue is to gather under the same umbrella statistical guidelines as simple and structured tools to be easily understood by a physician and replicated by researchers. The manuscripts must clearly and easily explain information about what the methods are, how they work, and their clinical applicability, as instruments for critical appraisal of medical scientific literature. Statistical methods and applications in medicine, dental medicine, and veterinary medicine will be appreciated.
Prof. Dr. Sorana D. Bolboacă
Ass. Prof. Dr. Polychronis Kostoulas
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 papers will be 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 1600 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.
- Statistical data analyses
- Statistical guideline
- Statistical data interpretation
- Computing statistics
- Bayesian true prevalence estimation of disease
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Definition and Estimation of Covariate Effect Types for Treatment Effectiveness
Authors: Yasutaka Chiba
Affiliation: Clinical Research Center, Kinki University Hospital 377-2, Ohno-higashi, Osakasayama, Osaka 589-8511, Japan
Abstract: In some clinical studies, researchers are interested in assessing covariate effect types that indicate whether a covariate is predictive and/or prognostic, in addition to endpoint evaluation. Recently, for a case with a binary outcome, Chiba (Clinical Trials, 2019; 16: 237-245) proposed a new concept of the covariate effect type, which is assessed in terms of four response types of potential outcomes, and showed that currently standard subgroup or regression analysis, which is assessed by two types, can deal with only a special case. Although his concept can be a potentially valuable alternative to currently standard analysis, its application is limited into a case with a binary outcome. In this article, we aim to generalize his concept to be applicable to continuous and time-to-event outcomes. Following his concept, we define covariate effect types by applying four response types of potential outcomes. As it is difficult to estimate the four types from the observed data without making any assumption, we propose an estimation method under the assumption of independent potential outcomes. Our approach is illustrated using data from a clinical study with a time-to-event outcome.