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Total 793 results found since Jan 2013.

Uncovering cross-bridge properties that underlie the cardiac active complex modulus using model linearisation techniques
Math Biosci. 2022 Oct 18:108922. doi: 10.1016/j.mbs.2022.108922. Online ahead of print.ABSTRACTThe properties underlying cardiac cross-bridge kinetics can be characterised by a muscle's active complex modulus. While the complex modulus can be described by a series of linear transfer functions, the biophysical mechanisms underlying these components are represented inconsistently among existing cross-bridge models. To address this, we examined the properties commonly implemented in cross-bridge models using model linearisation techniques and assessed their contributions to the complex modulus. From this analysis, we develope...
Source: Mathematical Biosciences - October 21, 2022 Category: Statistics Authors: Julia H Musgrave June-Chiew Han Marie-Louise Ward Andrew J Taberner Denis S Loiselle Kenneth Tran Source Type: research

Retracted: Risk factors of Recurrent Stroke in Young and Middle-Aged Stroke Patients After Interventional Therapy
Comput Math Methods Med. 2022 Nov 27;2022:9898271. doi: 10.1155/2022/9898271. eCollection 2022.NO ABSTRACTPMID:36474911 | PMC:PMC9719806 | DOI:10.1155/2022/9898271
Source: Computational and Mathematical Methods in Medicine - December 7, 2022 Category: Statistics Authors: Computational And Mathematical Methods In Medicine Source Type: research

Bayesian adaptive determination of the sample size required to assure acceptably low adverse event risk
We describe a Bayesian adaptive procedure for determining if the sample size for a development program needs to be increased and, if necessary, by how much, to provide the required assurance of limited risk. The decision is based on the predictive likelihood of a sufficiently high posterior probability that the relative risk is no more than a specified bound. Allowance can be made for between‐center as well as within‐center variability to accommodate large‐scale developmental programs, and design alternatives (e.g., many small centers, few large centers) for obtaining additional data if needed can be explored. Binomi...
Source: Statistics in Medicine - October 4, 2013 Category: Statistics Authors: A. Lawrence Gould, Xiaohua Douglas Zhang Tags: Research Article Source Type: research

Mathematical model of the effect of ischemia-reperfusion on brain capillary collapse and tissue swelling.
Abstract Restoration of an adequate cerebral blood supply after an ischemic attack is a primary clinical goal. However, the blood-brain barrier may break down after a prolonged ischemia causing the fluid in the blood plasma to filtrate and accumulate into the cerebral tissue interstitial space. Accumulation of this filtration fluid causes the cerebral tissue to swell, a condition known as vasogenic oedema. Tissue swelling causes the cerebral microvessels to be compressed, which may further obstruct the blood flow into the tissue, thus leading to the no-reflow phenomenon or a secondary ischemic stroke. The actual m...
Source: Mathematical Biosciences - March 3, 2015 Category: Statistics Authors: Mohamed Mokhtarudin MJ, Payne SJ Tags: Math Biosci Source Type: research

Surveillance of cardiovascular diseases using a multivariate dynamic screening system
In the SHARe Framingham Heart Study of the National Heart, Lung and Blood Institute, one major task is to monitor several health variables (e.g., blood pressure and cholesterol level) so that their irregular longitudinal pattern can be detected as soon as possible and some medical treatments applied in a timely manner to avoid some deadly cardiovascular diseases (e.g., stroke). To handle this kind of applications effectively, we propose a new statistical methodology called multivariate dynamic screening system (MDySS) in this paper. The MDySS method combines the major strengths of the multivariate longitudinal data analysi...
Source: Statistics in Medicine - March 11, 2015 Category: Statistics Authors: Peihua Qiu, Dongdong Xiang Tags: Research Article Source Type: research

Leveraging prognostic baseline variables to gain precision in randomized trials
We focus on estimating the average treatment effect in a randomized trial. If baseline variables are correlated with the outcome, then appropriately adjusting for these variables can improve precision. An example is the analysis of covariance (ANCOVA) estimator, which applies when the outcome is continuous, the quantity of interest is the difference in mean outcomes comparing treatment versus control, and a linear model with only main effects is used. ANCOVA is guaranteed to be at least as precise as the standard unadjusted estimator, asymptotically, under no parametric model assumptions and also is locally semiparametric ...
Source: Statistics in Medicine - April 15, 2015 Category: Statistics Authors: Elizabeth Colantuoni, Michael Rosenblum Tags: Research Article Source Type: research

The choice of prior distribution for a covariance matrix in multivariate meta‐analysis: a simulation study
Bayesian meta‐analysis is an increasingly important component of clinical research, with multivariate meta‐analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta‐analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were ...
Source: Statistics in Medicine - August 24, 2015 Category: Statistics Authors: Sandra M. Hurtado Rúa, Madhu Mazumdar, Robert L. Strawderman Tags: Research Article Source Type: research

Statistical methods for studying disease subtype heterogeneity
A fundamental goal of epidemiologic research is to investigate the relationship between exposures and disease risk. Cases of the disease are often considered a single outcome and assumed to share a common etiology. However, evidence indicates that many human diseases arise and evolve through a range of heterogeneous molecular pathologic processes, influenced by diverse exposures. Pathogenic heterogeneity has been considered in various neoplasms such as colorectal, lung, prostate, and breast cancers, leukemia and lymphoma, and non‐neoplastic diseases, including obesity, type II diabetes, glaucoma, stroke, cardiovascular d...
Source: Statistics in Medicine - December 1, 2015 Category: Statistics Authors: Molin Wang, Donna Spiegelman, Aya Kuchiba, Paul Lochhead, Sehee Kim, Andrew T. Chan, Elizabeth M. Poole, Rulla Tamimi, Shelley S. Tworoger, Edward Giovannucci, Bernard Rosner, Shuji Ogino Tags: Tutorial in Biostatistics Source Type: research