Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

121
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
121
Study Design in Statistics01:15

Study Design in Statistics

7.8K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
7.8K
Biostatistics: Overview01:20

Biostatistics: Overview

222
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
222
Overview of Biostatistics in Health Sciences01:19

Overview of Biostatistics in Health Sciences

349
Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
349
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

184
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
184
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

498
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
498

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Navigating the Complexity and Heterogeneity of Thrombectomy Decision-Making for Medium-Vessel Occlusion Stroke: Where Do We Go From Here?

Stroke (Hoboken, N.J.)·2026
Same author

Stent retriever-assisted intra-arterial lysis for distal vessel occlusions: a feasible pharmacomechanical rescue strategy.

Journal of neurointerventional surgery·2026
Same author

Physician Approaches to Determining Goals of Stroke Care for Patients Living With Disability or Dementia: Results from the SEED Mixed-Methods Study.

Stroke·2026
Same author

Safety and Efficacy of Nerinetide at Year 1 in Participants Enrolled in ESCAPE-NEXT: A Multicenter, Double-Blind, Randomized Controlled Trial.

Journal of the American Heart Association·2026
Same author

Association of Successful Recanalization and Functional Outcomes in Minor Ischemic Stroke With Proven Occlusion: A Secondary Analysis of TEMPO-2 Trial.

Stroke·2026
Same author

Phase 2 Futility Trials in Alzheimer Disease and Mild Cognitive Impairment: A Cohort Analysis of the ADNI Data Set.

Neurology·2026

Related Experiment Video

Updated: Jun 9, 2025

Author Spotlight: Innovative Techniques and Future Directions in Stroke Research
04:46

Author Spotlight: Innovative Techniques and Future Directions in Stroke Research

Published on: May 5, 2023

3.6K

An Introduction to Bayesian Approaches to Trial Design and Statistics for Stroke Researchers.

Johanna M Ospel1,2, Scott Brown3, Jessalyn K Holodinsky2,4,5

  • 1Department of Diagnostic Imaging (J.M.O., S.B.C., M.D.H., M.G.), Foothills Medical Center, University of Calgary, AB, Canada.

Stroke
|October 22, 2024
PubMed
Summary

Bayesian statistics offer a probabilistic approach for stroke research, updating beliefs with data to interpret treatment effectiveness. This review explains Bayesian methods and compares them to traditional frequentist statistics for stroke trials.

Keywords:
Bayes theoremobservational studies as topicsample sizestatisticsstroke

More Related Videos

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

444
Modeling Stroke in Mice: Focal Cortical Lesions by Photothrombosis
06:07

Modeling Stroke in Mice: Focal Cortical Lesions by Photothrombosis

Published on: May 6, 2021

6.1K

Related Experiment Videos

Last Updated: Jun 9, 2025

Author Spotlight: Innovative Techniques and Future Directions in Stroke Research
04:46

Author Spotlight: Innovative Techniques and Future Directions in Stroke Research

Published on: May 5, 2023

3.6K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

444
Modeling Stroke in Mice: Focal Cortical Lesions by Photothrombosis
06:07

Modeling Stroke in Mice: Focal Cortical Lesions by Photothrombosis

Published on: May 6, 2021

6.1K

Area of Science:

  • Neurology
  • Biostatistics
  • Clinical Trials

Background:

  • Frequentist statistics dominate stroke research data analysis.
  • Bayesian statistics are increasingly adopted in stroke research.

Purpose of the Study:

  • To introduce fundamental Bayesian statistics concepts for stroke trials.
  • To compare Bayesian and frequentist approaches using trial data.
  • To guide the conduct and interpretation of Bayesian analyses in stroke research.

Main Methods:

  • Review of Bayesian statistical principles.
  • Comparative analysis of Bayesian and frequentist methods.
  • Exemplary data from a randomized stroke trial used for illustration.

Main Results:

  • Bayesian approach provides probabilistic interpretations of parameters, such as treatment efficacy.
  • Demonstration of how prior beliefs are updated with observed data in Bayesian analysis.
  • Clear comparison highlighting differences between frequentist and Bayesian statistical interpretations.

Conclusions:

  • Bayesian statistics offer a valuable framework for analyzing and interpreting stroke trial data.
  • The paradigm shift towards Bayesian methods enhances understanding of treatment effects.
  • Researchers can leverage Bayesian analysis for more nuanced insights into stroke research parameters.